Total 874 data entry job vacancies in Information Technology category in Hong Kong

Data engineer (Azure) – Synapse and Pyspark, Python, Datawarehouse and Azure Data Explorer, Azure Devops

Job Scope

• Design, review and development of Pyspark scripts. Testing, troubleshooting of data pipelines, orchestration.

• Designing and developing reports and dashboards in PowerBI,setting up access control with row level security DAX query experience.

• Establishing connections to source data systems such as on-prem databases, IOT devices, APIs.

• Managing the collected data in appropriate storage/data-base solutions e.g. file systems, SQL servers, Big Data platforms such as Hadoop, HANA, etc. as required by the specific project requirements.

• Design, development of relevant data pipelines using pyspark, copy data activities for batch ingestion.

• Performing data integration e.g. using database table joins, or other mechanisms at an appropriate level as required by the analysis requirements of the project.

• Deployment of pipeline artifacts from one environment to the other using Azure Devops.

Skills & Experience

• Bachelor’s Degree in Computer Science or Engineering with 2 years of experience in Azure Data engineering, Python, Pyspark or Big Data development.

• Sound Knowledge of Azure Synapse analytics for pipelines, orchestration, set up.

• 1-2 experience in Visualization design and development with Power BI. Knowledge on row-level security, access control.

• Sound experience in SQL, Datawarehouse, data marts, data ingestion with Pyspark and Python.

• Expertise in developing and maintaining ETL processing pipelines in cloud-based platforms such as AWS, Azure, etc. (Azure Synapse or data factory preferred)

• Team player with good interpersonal, communication, and problem-solving skills.

• Preferred to have Devops expertise.

Working hours :

8:30am to 6pm (Monday to Friday) onsite, no hybrid option.

• Minimum of Bachelor’s Degree in Computer Science, Computer Engineering, Machine Learning / Data Science / AI or related disciplines;
• Able to understand and apply a range of AI/ML  techniques for regression and classification
• Familiar with popular python packages ( e.g. pandas, matplotlib, scikit-learn, XGBoost, NLTK, spaCy )
• Understanding of LLM concepts (e.g. context windows, embeddings, chunking, token management) and architectures (e.g. RAG)
• Experience with context engineering techniques and prompt optimization strategies
• Proficient in git, SQL
• Proficient in Business Intelligence tools (e.g. Tableau, Qlik, MS PowerBI, Microstrategy)

Bonus skillsets:

• Proficient in modern programming languages (e.g. typescript, C#)
• Experience in cloud platform and services preferably in AWS
• Experience in Docker/Kubernetes
• Experience with web frameworks and full-stack development, including backend frameworks (e.g. FastAPI, Flask, Express.js) and RESTful API development, and frontend technologies (e.g. React, Vue.js, HTML/CSS, TypeScript)
• Experience with LLM frameworks (e.g. LangChain, LlamaIndex, Hugging Face Transformers)
• Knowledge of vector databases and embedding techniques (e.g. Pinecone, Chroma, FAISS)
• Understanding of AI agent frameworks and multi-agent systems
• Strong presentation skills and ability to explain technical concepts clearly to a non-technical audience
• Proficient in statistical software tools (e.g. R, SAS)

Product area

The AI and Infrastructure team is redefining what’s possible. We empower Google customers with breakthrough capabilities and insights by delivering AI and Infrastructure at unparalleled scale, efficiency, reliability and velocity. Our customers include Googlers, Google Cloud customers, and billions of Google users worldwide. 

We're the driving force behind Google's groundbreaking innovations, empowering the development of our cutting-edge AI models, delivering unparalleled computing power to global services, and providing the essential platforms that enable developers to build the future. From software to hardware our teams are shaping the future of world-leading hyperscale computing, with key teams working on the development of our TPUs, Vertex AI for Google Cloud, Google Global Networking, Data Center operations, systems research, and much more.

Job description

Our thirst for technology is a part of everything we do. The Data Center Engineering team takes the physical design of our data centers into the future. Our lab mirrors a research and development department -- cutting-edge strategies are born, tested and tested again. Along with a team of great minds, you take on complex topics like how we use power or how to run state-of-the-art, environmentally-friendly facilities. You're a visionary who optimizes for efficiencies and never stops seeking improvements -- even small changes that can make a huge impact. You generate ideas, communicate recommendations to senior-level executives and drive implementation alongside facilities technicians.

Additional job description

The Data Center Design Integration team is a multi disciplinary team of architects and Information and Communication Technology (ICT)/Telecommunications (Telecom) designers that work on next generation data center designs. We are a sub-team of the broader Data Center Technology and Systems (DCTS) organization.

As a Telecom Lead, you will be responsible for working with executive level engineers across all disciplines to develop integrated telecom communication designs at the product and top level assembly state of a data center. You will lead external consultants to develop coordinated construction level drawing packages for manufacturing and construction.

Qualifications

Job responsibilities

• Work with internal telecom engineers and network designers to rationalize requirements into products, conceptual one lines and conceptual rack or tray layouts to enable adequate telecom infrastructure.
• Work with cross-functional disciplines across architecture, civil, mechanical, electrical, controls, security on product development, and assembly integration of telecom infrastructure.
• Participate in internal and external ICT/telecom product and data center design reviews across the following disciplines: Telecom, Security, Controls and Networking.
• Review construction level drawings produced by internal and external vendors that document ICT/telecom scope for program reference.
• Review and enable best practices for converged network allocations across all mechanical, electrical, and controls products. Act as an escalation path for site localization of canonical design where local requirements necessitate engineering judgement on adjustments to ICT/telecom design.

Minimum qualifications

• Bachelor's degree in Mechanical Engineering, Electrical Engineering or IT Engineering, or equivalent practical experience.
• 5 years of experience working with external telecom vendors on telecom products.

Preferred qualifications

• Master's degree in Mechanical or Electrical or IT engineering.
• 6 years of experience as a Registered Communications Distribution Designer (RCDD) Certified professional with ICT/telecom scope within mechanical and electrical data center products.
• 6 years of experience as key contributor on ICT/telecom scope within mechanical and electrical data center products
• Experience with Autodesk Revit or other 3D modeling software developing telecom rack and tray layouts.
• Ability to travel to visit data center sites or manufacturing partners.

About Us

• Founded in 2012, ByteDance's mission is to inspire creativity and enrich life. With a suite of more than a dozen products, including TikTok, Lemon8, CapCut and Pico as well as platforms specific to the China market, including Toutiao, Douyin, and Xigua, ByteDance has made it easier and more fun for people to connect with, consume, and create content.​

Why Join ByteDance

• Inspiring creativity is at the core of ByteDance's mission. Our innovative products are built to help people authentically express themselves, discover and connect – and our global, diverse teams make that possible. Together, we create value for our communities, inspire creativity and enrich life - a mission we work towards every day.​

• As ByteDancers, we strive to do great things with great people. We lead with curiosity, humility, and a desire to make impact in a rapidly growing tech company. By constantly iterating and fostering an "Always Day 1" mindset, we achieve meaningful breakthroughs for ourselves, our Company, and our users. When we create and grow together, the possibilities are limitless. Join us.​

Diversity & Inclusion​

• ByteDance is committed to creating an inclusive space where employees are valued for their skills, experiences, and unique perspectives. Our platform connects people from across the globe and so does our workplace. At ByteDance, our mission is to inspire creativity and enrich life. To achieve that goal, we are committed to celebrating our diverse voices and to creating an environment that reflects the many communities we reach. We are passionate about this and hope you are too.​

Job highlights

• Positive team atmosphere, Paid leave, Meals provided

Team Introduction:

• Internal Audit is a global function responsible for providing independent assurance and evaluating the company's risk management, governance and internal control processes to determine if they are designed and operating effectively.
• The Internal Audit team plans and executes audit projects according to our risk-based audit plan by evaluating financial, compliance, operational, and IT processes and controls. We work with business functions in addressing risks and improving the control environment through timely and comprehensive audit work and tracking of remediation actions until completion.
• We are looking for data scientists and AI developers who will power our mission by building data products that enable and empower continuous auditing and the identification and discovery of risks throughout various verticals.
• You will be deploying your engineering, data analytics and data science skills to be part of the mission to build state-of-the-art analytics products for the audit team.

Responsibilities:

• Model Evaluation & Audit Frameworks: conduct audits on the model lifecycle from training through deployment and monitoring, ensuring compliance with quality, performance, fairness, and risk-management standards.
• Risk Identification & Mitigation: Identify model vulnerabilities including bias, fairness violations, harmful hallucinations, security risks, and recommend remediation strategies.
• Measurement Metrics & Statistical Validation: Define and assess model performance metrics (accuracy, precision/recall, F1, calibration, robustness, fairness metrics), measurement of hallucination rates in LLMs, bias/fairness quantification, confidence scoring, and stability analyses.
• Communication & Collaboration: Develop and maintain collaborative working relationships with stakeholders, including data partners and owners across different business verticals.
• Clearly communicate technical findings, risk assessments, and recommendations to technical and non-technical stakeholders.
• Data Analytics Services: Partner with auditors to provide data support and guidance for audit engagements, including conducting interviews, observing systems and operations, developing queries and testing strategies, deploying data quality checks to ensure completeness and accuracy for data sets, and deriving insights.
• Data Warehousing: develop and maintain data warehouses across different business verticals to efficiently support audit engagements; implement data quality checks for key data assets and continuously collaborate with data partners to maintain completeness and accuracy of these assets.
• Automation and self-service analytics: partner with auditors to identify and analyze key risk indicators, contribute to a continuous auditing data strategy that will translate into various use cases and corresponding data solutions that can automate the evaluation of the design and effectiveness of controls; build and maintain ETL data pipelines, as well as dashboards to support the solutions.
• AI-Driven Automation and Insights: Leverage machine learning and AI to automate business and audit processes, surface insights from unstructured and structured data, and extend the team’s ability to deliver actionable recommendations at scale. Develop, train, and implement proprietary machine learning and AI models, to scale up audit testing insights.
• Professional Development: Continue to develop and expand knowledge in data analytics practices, machine learning, AI, and ByteDance products through continuous education. Provide data training to empower the audit team to derive insights.

Qualifications

Minimum Qualifications:

• Bachelor's degree in a quantitative discipline, such as Mathematics, Statistics, Computer Science, Financial Engineering, Operations Research, or Economics.
• Minimum of 5 years professional experience in applied data science, machine learning engineering, or AI research, specifically working with LLMs and traditional ML models and at least 5 years practical experience of data science or analytics from the technology sector, including but not limited to B2C SaaS, media tech, ecommerce, social media platforms, fintech etc.
• Proficiency in frameworks for auditing models, including criteria like robustness, fairness, interpretability, alignment, and compliance. Familiarity with emerging LLM auditing methodologies such as LLMAuditor (probe generation/answering cycles, human-in-the-loop assessments).
• Hands-on experience in designing, deploying, and monitoring large-scale ML models with thorough understanding of lifecycle risks and controls plus strong proficiency in SQL and Python (including libraries such as Hugging Face Transformers, TensorFlow, PyTorch, scikit-learn), data analysis tools, and ML pipeline orchestration platforms.
• Expertise in defining and assessing model performance metrics (accuracy, precision/recall, F1, calibration, robustness, fairness metrics), measurement of hallucination rates in LLMs, bias/fairness quantification, confidence scoring, and stability analyses.
• Extensive knowledge of transformer-based LLM architectures (e.g., GPT, BERT, T5, PaLM) and classical ML algorithms (e.g., regression, tree-based methods, neural networks).
• Working knowledge of classical ML algorithms and LLM architecture and deep technical expertise in LLMs and Traditional ML and a proven track record supporting or performing AI/ML model audits or evaluations within a corporate, regulatory, or advisory context.

Preferred Qualifications:

• PHD degree in a quantitative discipline, such as Mathematics, Statistics, Computer Science, Financial Engineering, Operations Research, or Economics.
• Proficiency in frameworks for auditing models, including criteria like robustness, fairness, interpretability, alignment, and compliance.
• Familiarity with emerging LLM auditing methodologies such as LLM Auditor (probe generation/answering cycles, human-in-the-loop assessments).
• Ability to analyze model design, training methods, data pipelines, and inference behaviors.
• Capability to identify model vulnerabilities including bias, fairness violations, harmful hallucinations, security risks, and to recommend remediation strategies.
• Experience building and maintaining data analytics solutions for continuous audit programs, including automating common analyses and recurring checks plus the ability to clearly communicate technical findings, risk assessments, and recommendations to technical and non-technical stakeholders.
• Experience with data integration, ETL processes, and large-scale data processing systems plus working knowledge of cloud-based infrastructure such as AWS, GCP, Azure or Snowflake; working knowledge of large scale data processing techniques, such as Hadoop, Flink and MapReduce and a good understanding of data warehouse and data modeling principles.
• Front end and back end software development skills.

Responsibilities

• Design, develop, and maintain end to end data pipelines for ingesting, transforming, and delivering data from multiple source systems (databases, files, APIs, streaming platforms).

• Build and optimize ETL / ELT workflows using SQL, Python, and enterprise data integration tools.

• Ensure data pipelines are scalable, resilient, and performant to meet operational and analytical requirements.

Database & Data Platform Management

• Work hands on with RDBMS platforms such as Oracle, DB2, SQL Server, or PostgreSQL for data extraction, transformation, and performance tuning.

• Develop and optimize SQL queries, views, and stored procedures to support reporting and analytics use cases.

• Support data modelling activities (logical and physical) for analytics and reporting layers.

Data Quality, Governance & Operations

• Implement data validation, reconciliation, and monitoring to ensure data accuracy, completeness, and consistency.

• Support operational data activities, including incident investigation, root cause analysis, and remediation.

• Maintain clear documentation for data pipelines, schemas, and operational processes to support audits and knowledge transfer.

Collaboration & Stakeholder Engagement

• Collaborate with business users, product owners, and downstream teams to gather requirements and translate them into technical solutions.

• Work closely with Data Analysts, BI developers, and Data Scientists to enable dashboards, reports, and advanced analytics.

• Participate in Agile ceremonies and contribute to sprint planning, estimation, and delivery

Requirements

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or equivalent practical experience.

• Strong hands on experience with SQL and relational databases (Oracle, DB2, SQL Server, PostgreSQL).

• Experience building and supporting ETL / data pipelines in enterprise environments.

• Solid understanding of data modelling, data quality, and data lifecycle management.

• Ability to troubleshoot data issues and work in production / operational environments.

• Experience with Python for data processing or automation.

• Experience with data streaming technologies (e.g. Kafka, Spark, NiFi).

• Experience with BI and visualization tools such as Tableau, Qlik, or Power BI.

• Knowledge of cloud or hybrid data platforms and orchestration tools.

• Knowledge of Agile / DevOps practices and CI/CD for data pipelines.

Licence no: 12C6060

Defectivity Data Analytics Engineer

We are seeking a skilled Data Analytics Engineer to join our Defectivity Engineering team in the wafer fabrication fab. The ideal candidate will leverage data analytics, statistical methods, and engineering knowledge to identify, analyze, and reduce wafer defects, improving yield and process quality.

The Job

• The Defectivity department is a Site function, meaning that you will have the opportunity to work closely with both Defectivity Fab SG6/8 and Fab SG8E.
• Develop and implement data-driven models and algorithms to detect defect patterns and root causes.
• Collaborate with process engineers, equipment engineers, and quality teams to translate data insights into actionable process improvements.
• Support the integration of advanced analytics and machine learning techniques for predictive defect detection.
• Participate in cross-functional problem-solving teams to address complex defectivity challenges.
• Ensure compliance with fab safety, quality, and documentation standards.

Requirements

• Bachelor’s or Master’s degree in Engineering, Data Science, Statistics, or related field.
• Strong experience in data analytics, preferably in semiconductor manufacturing or wafer fabrication.
• Proficiency in programming languages such as Python, R, or SQL.
• Familiarity with statistical software and data visualization tools (e.g., Spotfire, Power BI).
• Understanding of semiconductor fabrication processes and defectivity mechanisms.
• Excellent problem-solving, communication, and teamwork skills.
• Ability to work in a fast-paced, dynamic manufacturing environment.

Analytics Center of Excellence (COE) at Income Insurance is the enterprise team that champions and develops AI/ML solutions. We focus on in-house solution development as well as partnerships. To ensure the successful execution of Income’s enterprise strategy to build Analytics as a product through impactful AI/ML solutions, driving innovation, and cultivating AI culture, we are seeking a highly skilled and experienced Data Scientist to join our team. The selected candidate will focus on building Generative AI-based solutions

Responsibilities:

• Take ownership of AI/ML use cases, from design and implementation to continuous enhancement.
• Act as a technical specialist in AI, primarily focusing on Generative AI. Design and develop solutions to address various business challenges, particularly those related to optimizing operational efficiency.
• Collaborate with other data engineers, analysts, data scientists, product specialists, and stakeholders to build well-crafted, pragmatic, and robust solutions that meet business requirements.
• Stakeholder management and engagement: proactively engage with stakeholders to understand their needs and translate them into technical requirements for AI/ML modeling.
• Maintain documentation of dataset curation, modeling approach, model performance, code changes, and workflows.
• Demonstrate a strong understanding of data privacy regulations such as PDPA, and AI governance guidelines to ensure compliance.
• Foster an innovative and growth-oriented mindset, continuously seeking opportunities to enhance AI/ML models and drive improvements across the organization.

Requirements:

• 3–5 years of experience in a data science role, with demonstrable expertise in the AWS platform. Experience with Microsoft Copilot Studio is a plus.
• Bachelor’s degree in computer science or equivalent.
• Familiarity with techniques for Document Chunking, Embedding, and Information Retrieval for improving model accuracy and relevance.
• Expertise in Prompt Engineering for designing and managing effective prompts.
• Hands-on experience with AWS Bedrock, including deploying solutions using foundation models and integrating them into scalable applications using APIs and orchestration tools.
• Experience in Agentic Workflow for maximizing the utility of models, including understanding user intent and context to drive meaningful interactions.
• In-depth knowledge of supervised and unsupervised ML models – linear & logistic regression, clustering, tree-based models like random forest, bagging, and boosting models.
• Proficient in SQL, Python, and Spark.
• Proven experience in implementing MLOps practices on AWS.
• Familiar with Data Warehouses such as Redshift, Hive, and S3.
• Passionate about technology and always looking to upskill based on new developments in the AI space.
• AWS or Microsoft certifications will be a plus.
• Experience in the financial industry, telecommunications, or consulting is preferred

Role Summary

The Data Center Operations & Customer Service Engineerprovides operational and technical support for production infrastructure andcustomer services. The role performs hands-on data center operations andsupports troubleshooting activities involving servers, networking equipment,and data center infrastructure.

Key Responsibilities

·      Respond to customer incidents and service requests according to SLA requirements.

·      Create and manage operational tickets and coordinate with engineering teams.

·      Perform routine data center inspections including servers, racks, cabling, and networking equipment.

·      Execute operational procedures such as rack/stack, cabling, and hardware component replacement.

·      Assist engineers with troubleshooting by collecting logs, hardware indicators, and diagnostic information.

·      Support monitoring systems and participate in incident response processes.

Required Qualifications

·      Bachelor’s degree in IT, Computer Science, Engineering, or equivalent practical experience.

·      2–5 years of experience in data center operations or infrastructure operations.

·      Hands-on experience with servers, networking equipment, and data center environments.

·      Basic Linux system administration and operational experience.

·      Understanding of networking fundamentals such as TCP/IP and switching.

·      Willingness and ability to work in a shift-based support model, including night shifts, weekends, and public holidays, in accordance with business operational needs and in compliance with MOM regulations.

Preferred Qualifications

·    Hands-on experience performing rack/stack, cabling, and hardware maintenance in data centers.

·      Experience troubleshooting server and network equipment.

·      Familiarity with monitoring systems and incident management tools.

·      Experience supporting large-scale infrastructure environments.

Main responsibilities:

• Provide day-to-day support for business applications and databases, resolving application bugs, database errors, and integrate new features in a timely manner
• Translate business rules and process needs into structured data requirements and system specifications
• Build and refine database frameworks to deliver accurate reporting and effective data extraction
• Develop conceptual, logical, and physical data models
• Assist with the implementation of application patches, upgrades, and database migrations
• Support and manage user access to applications and databases
• Conduct system and application tests to ensure functionality and performance before new releases or updates
• Monitor database performance, identify bottlenecks, and apply tuning measures to optimize performance
• In-charge of IT audits and BCP
• Ensure compliance with IT policies, security standards, and best practices
• Backup of IT system support tasks
• Other assigned duties

Requirements

• Diploma or Degree in Information Technology, Computer Science or related field
• 5 years experience in application and database system role
• Familiar with SICS and SAP Business Intelligent, highly preferred
• Good knowledge of MS SQL database and scripting languages
• Strong knowledge of database design, data modeling, and query optimization
• Good technical, analytical and problem-solving skills
• Excellent interpersonal and communication
• Ability to work independently and as part of a team

Interested applicants please send your resume to venessagoh@recruitexpress.com.sg

Venessa Goh Wee Ni

R24124686

Recruit Express Pte Ltd

EA License No: 99C4599

We regret that only shortlisted candidates will be contacted.

Key Responsibilities:

• Design, build, and maintain scalable, reliable data pipelines to support AI/ML models and analytics in global supply chain and planning operations.

• Collaborate with data scientists and AI engineers to prepare and transform structured and unstructured data for modeling and simulation.

• Develop and optimize ETL/ELT processes using tools like Databricks, Spark.

• Ensure data quality, versioning, and lineage across the ML lifecycle.

• Support the deployment and monitoring of AI models in production environments.

• Translate business requirements into technical solutions, working closely with cross-functional teams.

• Contribute to the development of data services and APIs that power intelligent decision-making tools.

Functional Knowledge

• Masters Degree in Data Science, Computing, AI/ Analytics

• 7-10 years of expertise in SQL, Python, and distributed data processing (e.g., Spark, PySpark).

• Experience with cloud-based data platforms (Azure).

• Experience with LLM and Agentic AI

• Familiarity with MLOps tools and practices (e.g., MLflow) is a strong advantage.

• Experience working with AI/ML frameworks (e.g., TensorFlow, PyTorch) or supporting model training and deployment is a strong advantage.

• Understands the role of data in driving supply chain and planning decisions.

• Able to align data engineering solutions with business KPIs and operational goals.

• May lead technical workstreams or mentor other engineers.

• Takes ownership of data infrastructure components and contributes to architectural planning.

• Solves complex data challenges with creativity and precision.

• Proactively identifies and resolves data quality, performance, and scalability issues.

• Enables the success of AI/ML initiatives by ensuring robust, scalable, and high-quality data infrastructure.

• Supports real-time and predictive decision-making across global operations

• Communicates effectively with data scientists, AI engineers, and business stakeholders.

• Comfortable working in a fast-paced, collaborative environment with evolving priorities.

Overview

• Analyze the current data landscape, client capabilities, and business requirements to design scalable, high‑performance, and highly available data solutions.
• Lead cross‑functional teams in designing and developing data solution configurations aligned with the proposed architecture.
• Recommend best‑fit approaches to address development, integration, and performance challenges in data warehouse and business intelligence environments.
• Define strategic direction and create implementation roadmaps for enterprise data initiatives.
• Serve as the primary point of contact for architecture, design, and development‑related queries.
• Apply deep expertise in both modern and traditional data technologies to guide architectural decisions.
• Identify, evaluate, and adopt emerging technologies and methodologies to improve performance, automation, and scalability.
• Support business development by shaping solution architectures for proposals and participating in client meetings with the sales team.

Experience and Qualifications

• 15+ years of experience in data engagements, leading data strategy, roadmaps, and end‑to‑end implementation programs.
• Expertise in enterprise data strategy, alignment with business objectives, transformation initiatives, and warehouse architecture models.
• Strong understanding of information flow, integration and interfacing approaches, business information models, logical and canonical data models, and data lifecycle management.
• Experience with analytical, regulatory, and compliance reporting models.
• Mandatory banking industry experience with strong domain expertise.
• Extensive experience in data migration strategy, planning, costing, execution, retention, archival, and disposal.
• Master Data Management (MDM) across multi‑domain environments using tools such as Informatica MDM, Stibo PIM, and Talend Data Fabric.
• Data governance capability assessment (as‑is/to‑be), framework design, model development, roadmap creation, and execution.
• Proficiency with architecture tools such as PowerDesigner, ArchiMate, Toad, and ERWin.
• Strong foundation in normalized/denormalized design, ER modeling, star schema design, hierarchical models, and Inmon and Kimball methodologies.
• Hands‑on experience with platforms including Postgres, Oracle, SQL Server, DB2, Teradata, Hive, Pig, Impala, MongoDB, Cassandra, HBase, and Snowflake.

Additional Skills

• Programming and scripting: MS SQL, PL/SQL, NoSQL, Shell scripting, VBA, T‑SQL, DAX.
• Reporting and UI tools: MS SSRS, Oracle Forms (6i, 9i, 10g), Intellimatch, MS Access.
• RDBMS experience: Oracle 9i/10g, MS SQL Server 2008–2014, Oracle 9i AS/10g.
• Web and cloud technologies: Sitecore, AWS, WordPress, Google Analytics/GTM.
• Management tools: MS Project, MS Office Suite, JIRA, rLean.
• Frameworks and certifications: TOGAF, DAMA‑DMBOK.

We are seeking a Data Architect to own the end-to-end data architecture for operational and analytics data within data centre environments. This role spans industrial data ingestion, data modeling, analytics enablement, and embedded data governance, ensuring OT data (e.g., OPC UA, BMS, PQMS) is transformed from raw telemetry into trusted, business-ready insights.

This is a hands-on architecture role responsible for defining standards, building critical components of the data platform, and ensuring operational reliability. The role serves as the technical lead for OT data ingestion, analytics architecture, and embedded data governance.

Make an impact by 

1. OT Data Engineering & Platform Architecture

• Design, build, and operate OPC UA-based data ingestion pipelines from BMS, PQMS, PLCs, and sensors.
• Implement edge and on-premises data pipelines suitable for data centre environments.
• Manage raw and curated data layers, ensuring reliability, consistency, and performance.
• Address time-series data challenges, including sampling rates, timestamps, aggregation strategies, and late-arriving data.
• Monitor, troubleshoot, and optimise production pipelines.

2. End-to-End Architecture Ownership

• Own and evolve the end-to-end data architecture from OT source systems to analytics consumption.
• Define and standardise:
• OPC UA connectivity and subscription patterns
• Streaming versus batch ingestion strategies
• Buffering, retry, and fault-tolerance mechanisms
• Establish architectural standards for:
• Time-series schemas
• Asset and tag hierarchies
• Naming conventions and metadata structures
• Define and uphold non-functional requirements across the platform, including:
• Availability and resilience
• Latency and performance
• Scalability
• Security at the OT/IT boundary
• Provide technical leadership and guidance on data architecture and design decisions.

3. Analytics Architecture & Enablement

• Transform curated OT data into analytics-ready fact and dimension models.
• Design and maintain data marts and datasets for dashboards and reporting.
• Define and govern the analytics and semantic layer to enable consistent KPI usage.
• Establish standards for metric calculation logic, grain definition, time windows, and aggregation rules.
• Ensure a single source of truth for business metrics and minimise duplication.
• Enable self-service analytics through well-documented and trusted datasets.

4. Data Governance, Quality & Lineage

• Embed data governance into pipelines and analytics models, including:
• Clear data ownership and domain attribution
• Technical metadata capture (tags, units, frequency, source)
• Define and enforce data quality rules (completeness, validity, timeliness).
• Ensure end-to-end lineage and traceability from OT source systems to business KPIs.
• Apply access controls and data security policies aligned with OT and enterprise standards.
• Maintain documentation to support auditability and transparency.
• Collaborate with stakeholders to ensure data is fit for purpose.

5. Collaboration & Continuous Improvement

• Partner with data analysts and stakeholders to translate business requirements into scalable analytics solutions.
• Validate analytics outputs against business intent and operational context.
• Act as a technical advisor on data usage, constraints, and interpretation.
• Drive continuous improvement of the data platform and analytics ecosystem.

Required Skills & Experience

• Extensive experience in data architecture, data engineering, analytics engineering, or industrial data platforms (typically gained over multiple years of progressive responsibility).
• Strong hands-on experience with OPC UA (clients, servers, security, certificates, subscriptions).
• Experience with BMS, PQMS, SCADA, or industrial telemetry systems.
• Strong programming skills in Python and proficiency in SQL.
• Experience with streaming and messaging technologies (e.g., Kafka, MQTT, or equivalent).
• Solid understanding of time-series data modeling.
• Experience working in on-premises or data centre environments.
• Hands-on experience with data quality management, lineage and metadata management, and metric governance or semantic modeling.
• Ability to balance architecture, delivery, and operational responsibilities.

Nice to Have

• Experience with hybrid cloud and on-premises data architectures.
• Experience in energy, facilities, or data centre operations.
• Exposure to analytics or machine learning use cases on operational data.
• Experience defining enterprise KPIs or analytics standards.

Key Responsibilities:

• Design, build, and maintain scalable, reliable data pipelines to support AI/ML models and analytics in global supply chain and planning operations.

• Collaborate with data scientists and AI engineers to prepare and transform structured and unstructured data for modeling and simulation.

• Develop and optimize ETL/ELT processes using tools like Databricks, Spark.

• Ensure data quality, versioning, and lineage across the ML lifecycle.

• Support the deployment and monitoring of AI models in production environments.

• Translate business requirements into technical solutions, working closely with cross-functional teams.

• Contribute to the development of data services and APIs that power intelligent decision-making tools.

Functional Knowledge

• Masters Degree in Data Science, Computing, AI/ Analytics

• 7-10 years of expertise in SQL, Python, and distributed data processing (e.g., Spark, PySpark).

• Experience with cloud-based data platforms (Azure).

• Experience with LLM and Agentic AI

• Familiarity with MLOps tools and practices (e.g., MLflow) is a strong advantage.

• Experience working with AI/ML frameworks (e.g., TensorFlow, PyTorch) or supporting model training and deployment is a strong advantage.

• Understands the role of data in driving supply chain and planning decisions.

• Able to align data engineering solutions with business KPIs and operational goals.

• May lead technical workstreams or mentor other engineers.

• Takes ownership of data infrastructure components and contributes to architectural planning.

• Solves complex data challenges with creativity and precision.

• Proactively identifies and resolves data quality, performance, and scalability issues.

• Enables the success of AI/ML initiatives by ensuring robust, scalable, and high-quality data infrastructure.

• Supports real-time and predictive decision-making across global operations

• Communicates effectively with data scientists, AI engineers, and business stakeholders.

• Comfortable working in a fast-paced, collaborative environment with evolving priorities.

We are seeking a dynamic and strategic Data Engineer to design, build, and scale enterprise-grade data platforms and pipelines for the product.

You will play a key role in shaping the product’s data strategy, ensuring high standards in data architecture, performance, scalability, and governance. Beyond core data engineering, you will drive innovation in AI-enabled capabilities, leveraging data as a foundation to unlock insights, improve decision-making, and enhance user outcomes across the public service.

This role requires strong technical expertise, analytical thinking, and stakeholder engagement skills to collaborate across product, engineering, policy, and business teams to deliver impactful, data-driven solutions.

Decision-making

· When making important decisions, we identify overarching goals, weigh up our options, identify impact, and clearly communicate and manage the risks and trade-offs of our decision.

Ownership

· In addition to technical responsibilities, this means having opinions on what is being done and having ideas on what should be done next. Building something that you believe in is the best way to build something good.

Continuous Learning

· Working on new ideas often means not fully understanding what you are working on. Taking time to learn new architectures, frameworks, technologies, and even languages is not just encouraged but essential.

Our Team

Our team is made up of passionate individuals committed to solving challenges and driving meaningful change. We embrace diversity of thought, encourage experimentation, and foster a culture of continuous learning. As part of this team, you will collaborate with Business Owner, Product Director, Product Managers, UX Designers, Engineers and Business Users.

Data Strategy, Engineering & Product Delivery

· Engage stakeholders to understand business challenges, refine use cases, and identify opportunities to harness data for product and policy decisions.

· Design, build, and maintain scalable data pipelines, data platforms, and reusable datasets to support analytics and product use cases.

· Develop data models and architectures to support complex business processes and reporting needs.

· Implement ETL/ELT processes to ingest, transform, and serve structured and unstructured data.

· Design and deliver analytics workflows, dashboards, and data products to enable insights and decision-making.

· Work iteratively with product teams to validate insights, refine analyses, and deliver measurable outcomes.

AI, Machine Learning & Advanced Analytics

· Develop and apply machine learning and Large Language Model (LLM)-based solutions, including prompt engineering and AI-enabled applications where relevant.

· Operationalise models through MLOps practices, including deployment, monitoring, and lifecycle management.

· Stay abreast of emerging trends in AI, data architectures, and optimisation techniques, and apply them to enhance product capabilities.

· Ensure responsible AI practices, including considerations for safety, fairness, and robustness.

Data Platforms, Integration & DataOps

· Design and implement moderately complex data systems and platforms, including supporting infrastructure and integrations.

· Apply advanced data integration techniques (e.g. streaming, Change Data Capture, message queues) to enable real-time and batch processing.

· Champion DataOps practices such as automated pipeline deployments, monitoring, and data observability.

· Ensure data quality, reliability, and integrity across systems and pipelines.

· Proactively manage and reduce technical debt to ensure long-term system stability.

Governance, Risk & Compliance

· Navigate data privacy, governance, and regulatory requirements, ensuring compliance with policies and standards.

· Identify risks in data systems and proactively address potential failure points.

· Align system design with security, performance, and compliance best practices across GovTech platforms.

Leadership, Collaboration & Delivery

· Own and deliver complex data engineering assignments across the full data lifecycle with minimal supervision.

· Break down complex problems into actionable tasks and coordinate delivery across teams.

· Drive high-quality, scalable delivery through engineering excellence and best practices.

· Mentor junior engineers and guide peers through code reviews, architecture discussions, and technical problem-solving.

· Collaborate effectively with Product Managers, Engineers, Designers, and Business stakeholders to deliver integrated solutions.

Strategic Alignment & Organisational Impact

· Contribute to team strategy, balancing short-term delivery with long-term platform sustainability.

· Evaluate new technologies and communicate trade-offs to technical and non-technical stakeholders.

· Participate in cross-team initiatives and align solutions with broader organisational goals.

Experience & Education

· Bachelor’s degree or higher in Data Science, Computer Science, Statistics, Applied Mathematics, or a related quantitative discipline.

· At least 5 years of experience in data engineering.

· Proven experience delivering data platforms or products in large-scale, enterprise environments.

Technical Skills

· Strong expertise in data modelling (OLTP, OLAP, dimensional modelling).

· Experience with cloud data platforms and tools (e.g. AWS, Azure Synapse/Microsoft Fabric, Snowflake, Databricks, Redshift, Data Lakes).

· Proficiency in big data technologies (e.g. Hadoop, Spark, Kafka, Flink).

· Strong programming skills in Python, Scala, or Java.

· Experience with CI/CD and DataOps practices (e.g. SHIP-HATS or equivalent).

· Expertise in data integration techniques (e.g. ETL/ELT, streaming, APIs).

AI & Machine Learning (Advantageous)

· Understanding of machine learning and LLM concepts, including prompt engineering and AI application development.

· Familiarity with MLOps practices and cloud-based ML deployment.

· Awareness of responsible AI principles (fairness, robustness, safety).

Core Competencies

· Strong analytical and problem-solving skills, with the ability to translate business problems into data solutions.

· Ability to design scalable, reliable, and maintainable data systems.

· Effective communication and stakeholder management skills.

· Self-driven, adaptable, and able to operate in ambiguity.

· Strong sense of ownership and commitment to delivery excellence.

· Passion for leveraging data and technology to deliver public good.