Total 902 data entry job vacancies in Information Technology category in Hong Kong

Minimum of 4 years of experience as a Data Analyst or similar role
Proficiency in PowerBI, Tableau, SQL, and Excel
Strong problem-solving skills with attention to detail
Excellent communication skills for presenting complex information
Ability to work independently as well as part of a team
Familiarity with statistical analysis tools is a plus
Senior Tableau and Power BI technical background
Can communicate with user fluently to gathering requirements, do data analysis and build dashboard.
Test and deploy to user. With Excellent retail data experience.

Data Analyst with knowledge and experience working with PathFin.ai FinTech product.

Building and maintaining comprehensive data analytics capabilities for the PathFin.ai programme to generate actionable insights

Developing analytics frameworks to track participant engagement, AI adoption trends, and programme effectiveness metrics

Creating interactive dashboards for programme monitoring, stakeholder reporting, and strategic decision-making

Identifying potential leads and opportunities for programme expansion based on data analysis

Generating automated reports on platform usage patterns, event attendance, and knowledge contribution rates

Providing data-driven recommendations to support programme growth, participant satisfaction, and operational efficiency

Precisely, we need the AI Engineering who can work on

• Creating AI Model (PathFin.ai preferable product or similar FinTech product) to generate insights.
• Building Dashboards using these insights.
• Drive recommendations for decision support systems.

Responsibilities:

• Collaborate with data scientists, coders, programmers and other stakeholders
• Creating and managing the AI development process and general infrastructure of a product
• Doing statistical analysis and interpreting findings
• Automating critical tasks and procedures for a data science team
• Creating data transformation infrastructure
• Developing AI models communicating the utility of the AI models they generate to a diverse range of employees inside the organisation, including product managers and management executives
• Exposing machine learning models via APIs with which other apps may interact
• Work on API and integrate systems.

Requirements:

• Master / Bachelor's degree in computer science, computer engineering, or relevant field.
• A minimum of 4+ years' experience in a similar role.
• Strong knowledge of Datawarehouse / database / Cloud systems and data mining would be preferred.
• Excellent organizational and analytical abilities.

Job Description:

* Design, develop, create, testing and maintain data pipeline ETL solutions to meet business, technical, user requirements.

* Collect and refine new data set. Maintain comprehensive documentation and data mapping across multiple systems.

* This role will involve creating scalable and optimized data models, ensuring they are aligned with best practices and organization's data architecture standards.

* Perform code review and testing.

* Drive optimization, testing to improve data quality and review solution design for data pipelines

* Ensure that proposed solutions are aligned and conformed to the big data architecture guidelines and roadmap.

* Ensure the reliability and accuracy of the data being processing is crucial. The engineer will establish robust monitoring, logging, and alerting to track pipelines' performance and data quality.

* Adopt the best data engineering practices to design and build reliable data marts in the Hadoop ecosystem for planning, reporting and analytics.

* Collaborate with cross-functional teams, such as stakeholders, data scientists and data analysts to align the logics of key metrics, and to ensure code logics correctly reflects latest business definitions.

* Maintain and optimize data pipelines to ensure all data are up to date with data accuracy and integrity.

* All code must be stored in a central repository with clear branching strategies and well documented commit messages.

* Work across various stakeholders to ensure smooth production deployment of data pipeline and adherence to data governance policies.

* Proactively identify and suggest solutions to improve data engineering process.

* Architect end-to-end solution for insurance data modelling in data warehouse and work with data engineering team on acquiring of data, contextualizing data for business analytics and integrating of product with business process and implementing insurance data modelling.

* Business process owner for onboarding business users and data products onto data platform and the data pipelines that feeds into dashboards or statistical model

Mandatory Skillset:

10+ Years of experience in Data related projects

Strong proficiency in Python, Hadoop, Hive, Spark, CDP

Good experience in Insurance or Banking domain is added advantage

User handling experience as this position require the individual to work with Business users directly

Responsibilities:

• Data Infrastructure: Build, implement, and maintain scalable data pipelines and storage architectures.
• Data Quality: Maintain strong data integrity by ensuring reliability, consistency, and accuracy across all data processes.
• Performance Optimization: Enhance system efficiency by tuning data processing workflows and improving large-scale performance.
• Process Improvement & Mentoring: Strengthen data engineering practices while providing guidance and support to junior engineers.
• Cross-Functional Collaboration: Partner with business analysts, data scientists, and backend teams to deliver timely and actionable data solutions.

Qualifications:

• 5+ years’ experience in data engineering or backend roles, with strong programming skills in Python or Java/Scala.
• Hands-on experience with big data tools: Spark, Flink, Kafka, Airflow, and cloud platforms (AWS, GCP, Azure); proficient in SQL.
• Skilled in designing and maintaining data models, warehouses, and ETL/ELT pipelines for scalable, reliable systems.
• Knowledgeable in data quality, governance, security, and compliance best practices.

We regret only shortlisted candidates will be contacted. All applications will be handled confidentially. By submitting your application, you agree to the collection, use, retention, and disclosure of your personal information to prospective employers.

Wecruit Pte Ltd
EA License No: 20C0270
EA Personnel: Nyon Hock Sen (Aden)
EA Personnel Reg No: R24124070

JOB OVERVIEW:

The Data Scientist (AI Engineer) serves as the critical technical bridge between high-level business strategy and scalable AI production. This role is dedicated to accelerating demand creation by translating complex client challenges into feasible, high-impact AI/ML prototypes and Proof of Concepts (PoCs). By balancing technical rigor with strategic communication, the incumbent assesses data maturity, designs agile solutions using both traditional and Generative AI, and ensures that all innovations are architecturally sound and ready for production handover. Ultimately, this role drives organizational growth by de-risking AI investments and demonstrating the tangible technical value of emerging technologies.

DUTIES AND RESPONSIBILITIES:

Technical Requirements Elicitation & Analysis:

● Collaborate with AI/ML Business Analysts and clients to deeply understand their existing technical landscape, data infrastructure, and specific operational challenges.

● Translate high-level business needs into technical requirements for AI/ML solutions, identifying necessary data inputs, system integrations, and potential technical constraints.

● Assess the technical feasibility of proposed AI/ML use cases, ensuring alignment with the organization’s vetted tech stack and TRL standards, given the client's current data capabilities and technology stack. AI/ML Solution Conceptualization & Prototyping:

● Design and develop rapid, interactive prototypes or proof-of-concepts (PoCs) using appropriate AI/ML techniques (both traditional ML and cutting-edge AI like Generative AI).

● Demonstrate the functionality and potential business impact of AI/ML solutions through compelling technical demonstrations and visualizations.

● Iterate rapidly on prototypes based on client feedback, showcasing agility and responsiveness.

Technical Communication & Value Articulation:

● Simplify complex AI/ML concepts, algorithms, and technical architectures into understandable terms for non-technical business stakeholders. ● Clearly articulate the technical value proposition of proposed solutions, explaining how the AI/ML will work and why it is the right approach for the client's problem.

● Present technical findings, prototype results, and architectural considerations to clients and internal teams.

Organizational Data Capability Assessment (Technical):

● Conduct technical assessments of client data maturity, including data quality, accessibility, governance, and existing infrastructure.

● Advise clients on technical prerequisites and potential roadmaps for enhancing their data capabilities to support AI/ML initiatives.

Emerging Technology Research & Application:

● Actively research and evaluate new AI/ML technologies, tools, and frameworks (e.g., specific algorithms, cloud AI services, MLOps tools) relevant to demand creation and prototyping.

● Support the AI Solution Enablement Lead by exploring specific application-layer use cases for technologies already on the internal Tech Radar. Provide field feedback on the performance of vetted tools in client prototypes

● Identify how emerging technical capabilities can be applied to solve client problems and create new service offerings.

● Contribute technical insights to market exploration and strategic planning.

Cross-Functional Collaboration & Handover:

● Work in close partnership with the Data Science / AI Production Team from the earliest stages to ensure that prototyped solutions are designed with production scalability and maintainability in mind.

● Collaborate primarily with the AI Solution Enablement Lead to transition validated TRL 3 prototypes into the hardening phase. Ensure all initial technical elicitation is documented according to Enablement Quality Gate standards to prevent technical debt during handover

● Facilitate smooth technical handovers of validated concepts, prototypes, and initial architectural considerations to the production team, minimizing rework and ensuring continuity.

● Provide technical context and support during the transition phase to ensure the production team has a comprehensive understanding of the solution's intent and design.

● Problem Solving & Innovation:

● Apply strong analytical and problem-solving skills to technical challenges encountered during the demand creation and prototyping phases.

● Propose innovative technical approaches to address client needs and differentiate our solutions.

QUALIFICATIONS AND EXPERIENCES:

● Minimum Bachelor’s degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, or a related quantitative field. Master's or Ph.D. preferred.

● At least 5+ years of progressive experience in AI/ML solution design, data science, or AI engineering, with a significant portion in a client-facing or consulting capacity.

● Proven experience in designing, developing, and deploying functional AI/ML prototypes that demonstrate business value and are engineered for production.

● Expert proficiency in Python and extensive experience with core AI/ML libraries (e.g., scikit-learn, TensorFlow, PyTorch).

● Demonstrated hands-on experience with Generative AI and Large Language Models (LLMs), including frameworks like LangChain, for designing and prototyping novel applications.

● Strong practical experience with Google Cloud Platform (GCP) services relevant to AI/ML (e.g., Vertex AI, BigQuery, Cloud Storage, Dataflow, Cloud Functions).

● Proficiency with Docker for containerization and a foundational understanding of container orchestration (e.g., Kubernetes concepts for GKE).

● Solid understanding of MLOps principles for reproducibility, experiment tracking (e.g., MLflow, Vertex AI ML Metadata/Model Registry), and model versioning.

● High-level expertise in data governance frameworks, data quality assessment (including tools/libraries like Great Expectations), metadata management (e.g., Google Cloud Data Catalog), and data architecture principles.

● Strong expertise in distributed computing and parallel processing techniques is vital for handling large-scale AI workloads.

● A proven track record of innovations and executions in deep learning, demonstrated through shipping products or first-author publications at leading AI conferences, is a strong differentiator.

● Experience in the container shipping industry or related logistics/supply chain domains is highly advantageous.

● Excellent presentation, documentation, and stakeholder management skills.

Working hours:

Mon to Fri, 8:30am to 5:30pm

The Opportunity

Join a team building the data foundations that support the firm’s AI and analytics capabilities. This role sits within the engineering effort to develop a modern Lakehouse and AI data platform that enables reliable, well-governed and high-performing data use across the firm.

At Goldman Sachs, engineering teams are positioned at the centre of the business, building scalable systems, solving complex technical problems and turning data into action. In data engineering roles, the emphasis is on designing, building and maintaining large-scale data platforms, delivering production pipelines, improving reliability and quality, and partnering closely with users of the platform.

This is a delivery-focused role for engineers who want to build robust data assets in production, work with modern data technologies, and grow over time within the firm. You will contribute to the data models, pipelines and platform capabilities that underpin analytics, operational decision-making and emerging AI use cases, and may also help extend platform tooling where additional functionality is needed.

Role Summary

As a Data Engineer in the Lakehouse and AI Data Platform team, you will design, build, test and support data pipelines and curated datasets on the firm’s modern data platform. You will work across ingestion, transformation, modelling, optimisation and data quality, helping to deliver data products that are reliable, scalable and fit for purpose.  Where there are gaps in platform functionality, you may also contribute to shared tooling or framework components that improve how the platform is used and operated.

The role is suited to engineers who are comfortable writing code, working with SQL and distributed data processing, and solving practical delivery problems in a team environment. More experienced candidates may also contribute to technical design, platform standards and the shaping of delivery approaches across a wider set of use cases.

Key Responsibilities

Pipeline Engineering

• Build, enhance and support batch and streaming data pipelines on the Lakehouse and AI data platform.
• Refactor or modernise existing data flows where needed to improve reliability, performance and maintainability.
• Where needed, build reusable tooling to improve delivery, consistency and operational support.
• Ensure data pipelines are production-ready, well tested and operationally supportable.

Data Modelling and Curation

• Develop raw, refined and curated datasets that support analytics, reporting and AI use cases.
• Apply sound data modelling principles to represent business entities, relationships and historical change accurately.
• Work with consumers to shape data products that are usable, well documented and aligned to business needs.

Data Quality and Reconciliation

• Implement controls to validate completeness, accuracy and consistency of data across pipelines and datasets.
• Use reconciliation approaches to build confidence in production outputs and investigate breaks where they arise.
• Contribute to clear standards for testing, monitoring and issue resolution.
• Contribute to practical improvements in testing, monitoring or reconciliation tooling where these strengthen platform reliability and day-to-day delivery.

Delivery and Partnership

• Work closely with engineers, platform teams and data consumers to deliver agreed outcomes to time and quality expectations.
• Communicate clearly on progress, risks, dependencies and design choices, including where delivery would benefit from improvements to shared platform tooling.
• For more senior candidates, take a broader role in technical leadership, task breakdown and support for junior engineers.

Skills and Experience

Required

• Bachelor’s or master’s degree in a relevant discipline, or equivalent practical experience, with evidence of strong quantitative skills or data engineering expertise.
• Strong hands-on programming experience in Python or Java.
• Good working knowledge of SQL, including troubleshooting, optimization and data analysis.
• Ability to learn new tools, internal platforms and delivery workflows quickly.
• Familiarity with software engineering fundamentals, including version control, testing, release discipline and CI/CD practices.

Data Engineering Capability

• Understanding of temporal data modelling, including the handling of historical state and change over time.
• Knowledge of schema design, schema evolution and data compatibility considerations.
• Understanding of partitioning, clustering and other techniques used to improve data performance at scale.
• Ability to make sensible design choices across normalized and denormalized models, and between natural and surrogate keys.
• Practical approach to data quality, reconciliation and root-cause analysis.
• Experience building or supporting production data pipelines in a collaborative engineering environment.
• Experience working with distributed data processing frameworks such as Apache Spark.
• Working knowledge of common data formats such as JSON, Avro and Parquet.

Technology Environment

The role will involve working with a modern and evolving data stack. Candidates are not expected to have deep expertise in every tool from day one but should bring relevant experience and the ability to work across comparable technologies.

Examples of technologies in scope include:

• Data processing and logic: ANSI SQL, Apache Spark, Kafka
• Data formats: JSON, Avro, Parquet
• Platforms and storage: Snowflake, Apache Iceberg, Databricks, Hadoop ecosystem technologies, Sybase IQ
• Engineering and deployment: CI/CD tooling, containerized or Kubernetes-based deployment approaches where relevant

You will also work with internal data management and platform tooling, so a practical and adaptable engineering mindset is important.

What We Are Looking For
We are looking for engineers who can deliver well-structured, reliable solutions in production and who take ownership of the quality of what they build. The role suits candidates who are technically strong, pragmatic and comfortable working in a fast-paced environment where data platforms support important business outcomes. It will also suit candidates who are willing to contribute to shared tooling or platform components that make the wider engineering environment more effective.

Stronger candidates will typically demonstrate:

• sound judgement in technical trade-offs
• attention to detail in data correctness and testing
• a clear and structured approach to problem solving
• willingness to work closely with stakeholders and partner teams
• an ability to identify when delivery problems would be better solved through reusable tooling or platform improvements
• an interest in developing long-term expertise within the firm

ABOUT GOLDMAN SACHS

At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world.

We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers.

We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html

© The Goldman Sachs Group, Inc., 2026. All rights reserved.

Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law.

The Opportunity

Join a team building the data foundations that support the firm’s AI and analytics capabilities. This role sits within the engineering effort to develop a modern Lakehouse and AI data platform that enables reliable, well-governed and high-performing data use across the firm.

At Goldman Sachs, engineering teams are positioned at the centre of the business, building scalable systems, solving complex technical problems and turning data into action. In data engineering roles, the emphasis is on designing, building and maintaining large-scale data platforms, delivering production pipelines, improving reliability and quality, and partnering closely with users of the platform.

This is a delivery-focused role for engineers who want to build robust data assets in production, work with modern data technologies, and grow over time within the firm. You will contribute to the data models, pipelines and platform capabilities that underpin analytics, operational decision-making and emerging AI use cases, and may also help extend platform tooling where additional functionality is needed.

Role Summary

As a Data Engineer in the Lakehouse and AI Data Platform team, you will design, build, test and support data pipelines and curated datasets on the firm’s modern data platform. You will work across ingestion, transformation, modelling, optimisation and data quality, helping to deliver data products that are reliable, scalable and fit for purpose.  Where there are gaps in platform functionality, you may also contribute to shared tooling or framework components that improve how the platform is used and operated.

The role is suited to engineers who are comfortable writing code, working with SQL and distributed data processing, and solving practical delivery problems in a team environment. More experienced candidates may also contribute to technical design, platform standards and the shaping of delivery approaches across a wider set of use cases.

Key Responsibilities

Pipeline Engineering

• Build, enhance and support batch and streaming data pipelines on the Lakehouse and AI data platform.
• Refactor or modernise existing data flows where needed to improve reliability, performance and maintainability.
• Where needed, build reusable tooling to improve delivery, consistency and operational support.
• Ensure data pipelines are production-ready, well tested and operationally supportable.

Data Modelling and Curation

• Develop raw, refined and curated datasets that support analytics, reporting and AI use cases.
• Apply sound data modelling principles to represent business entities, relationships and historical change accurately.
• Work with consumers to shape data products that are usable, well documented and aligned to business needs.

Data Quality and Reconciliation

• Implement controls to validate completeness, accuracy and consistency of data across pipelines and datasets.
• Use reconciliation approaches to build confidence in production outputs and investigate breaks where they arise.
• Contribute to clear standards for testing, monitoring and issue resolution.
• Contribute to practical improvements in testing, monitoring or reconciliation tooling where these strengthen platform reliability and day-to-day delivery.

Delivery and Partnership

• Work closely with engineers, platform teams and data consumers to deliver agreed outcomes to time and quality expectations.
• Communicate clearly on progress, risks, dependencies and design choices, including where delivery would benefit from improvements to shared platform tooling.
• For more senior candidates, take a broader role in technical leadership, task breakdown and support for junior engineers.

Skills and Experience

Required

• Bachelor’s or master’s degree in a relevant discipline, or equivalent practical experience, with evidence of strong quantitative skills or data engineering expertise.
• Strong hands-on programming experience in Python or Java.
• Good working knowledge of SQL, including troubleshooting, optimization and data analysis.
• Ability to learn new tools, internal platforms and delivery workflows quickly.
• Familiarity with software engineering fundamentals, including version control, testing, release discipline and CI/CD practices.

Data Engineering Capability

• Understanding of temporal data modelling, including the handling of historical state and change over time.
• Knowledge of schema design, schema evolution and data compatibility considerations.
• Understanding of partitioning, clustering and other techniques used to improve data performance at scale.
• Ability to make sensible design choices across normalized and denormalized models, and between natural and surrogate keys.
• Practical approach to data quality, reconciliation and root-cause analysis.
• Experience building or supporting production data pipelines in a collaborative engineering environment.
• Experience working with distributed data processing frameworks such as Apache Spark.
• Working knowledge of common data formats such as JSON, Avro and Parquet.

Technology Environment

The role will involve working with a modern and evolving data stack. Candidates are not expected to have deep expertise in every tool from day one but should bring relevant experience and the ability to work across comparable technologies.

Examples of technologies in scope include:

• Data processing and logic: ANSI SQL, Apache Spark, Kafka
• Data formats: JSON, Avro, Parquet
• Platforms and storage: Snowflake, Apache Iceberg, Databricks, Hadoop ecosystem technologies, Sybase IQ
• Engineering and deployment: CI/CD tooling, containerized or Kubernetes-based deployment approaches where relevant

You will also work with internal data management and platform tooling, so a practical and adaptable engineering mindset is important.

What We Are Looking For
We are looking for engineers who can deliver well-structured, reliable solutions in production and who take ownership of the quality of what they build. The role suits candidates who are technically strong, pragmatic and comfortable working in a fast-paced environment where data platforms support important business outcomes. It will also suit candidates who are willing to contribute to shared tooling or platform components that make the wider engineering environment more effective.

Stronger candidates will typically demonstrate:

• sound judgement in technical trade-offs
• attention to detail in data correctness and testing
• a clear and structured approach to problem solving
• willingness to work closely with stakeholders and partner teams
• an ability to identify when delivery problems would be better solved through reusable tooling or platform improvements
• an interest in developing long-term expertise within the firm

ABOUT GOLDMAN SACHS

At Goldman Sachs, we commit our people, capital and ideas to help our clients, shareholders and the communities we serve to grow. Founded in 1869, we are a leading global investment banking, securities and investment management firm. Headquartered in New York, we maintain offices around the world.

We believe who you are makes you better at what you do. We're committed to fostering and advancing diversity and inclusion in our own workplace and beyond by ensuring every individual within our firm has a number of opportunities to grow professionally and personally, from our training and development opportunities and firmwide networks to benefits, wellness and personal finance offerings and mindfulness programs. Learn more about our culture, benefits, and people at GS.com/careers.

We’re committed to finding reasonable accommodations for candidates with special needs or disabilities during our recruiting process. Learn more: https://www.goldmansachs.com/careers/footer/disability-statement.html

© The Goldman Sachs Group, Inc., 2026. All rights reserved.

Goldman Sachs is an equal opportunity employer and does not discriminate on the basis of race, color, religion, sex, national origin, age, veterans status, disability, or any other characteristic protected by applicable law.

Job Reference No:

M-2025-1505

Job Role:

Augmentation Consultant

Years of Experience

At least 4 - 5 years’ experience in business analysis, system support, development, implementing system and maintaining IT systems

Role and Responsibilities

Role and Responsibilities

• Improve IT Operation business process and documentations.

• Triage of issues, including logging, tracking, routing and

categorizing reported incidents and service requests.

• Support users and provide resolutions for L1 issues, including

common problem / issue in FAQ, inquiries and essential

troubleshooting tasks.

• Managing of the service desk common mailbox.

• Prepare monthly report and support User Access Matrix review.

• Communication of operation matters to stakeholders (e.g.

system downtime, new implementations)

Requirements / Qualifications

Requirements / Qualifications

• Bachelor’s degree in Computer Science, Information

Technology, or a related field.

• Strong analytical and problem-solving skills.

• Excellent communication and interpersonal skills

• Ability to work collaboratively in a team environment.

Thanks, and Best Regards

Lini

Recruitment Consultant

R22108463

Amazon Web Services is a fast paced technology company and a leader in the world of Data Centers. We are looking for proven Data Center Technology Managers with experience in people management, strong technical understanding and the drive and vision to take Amazon.com Data Center Operations to the next level. The company's infrastructure consists of various custom built and off-the-shelf applications operating on a variety of platforms and other major technologies. The successful candidate works in a non-shift role while having experience managing staff on a 7/24 rotating shift, Problem Change & Incident Management, creating & tracking expenditure, devising and implementing improvements, mentoring people in all levels, supporting Infrastructure projects and proposing technical solutions.

Key job responsibilities

- Management of the team of Data Center Technicians; this includes all aspect of people/performance/rotation management. - Ensuring effective and efficient management of day to day 24x7 Datacenter Operations. - Manage/Improve the workflows and throughput for data Centers Operations. - Become a subject master in Data Center Operations. - Ensure all operational KPIs and Metrics are being measured and met inside his/her Data Centers(s). - Play a key part in innovation and driving automation within Data Center Operations. - Be passionate about the quality and quantity of services being provided by the Data Center Operations team and continuously strive to improve our Customer Experience. - Management of large scale events in the Datacenter(s). - Part of an on-call rotation for Datacenter Issue escalations. - Vendor management/monitoring vendor performance. - Ensuring his/her Data Center is compliant with all relevant security policies and procedures.

A day in the life

The successful candidate will have experience creating/devising strategies, mentoring people in all levels, sponsoring projects and proposing technical solutions. This position involves leading teams in hardware and network diagnostics followed by physical repair and includes participation in an on-call rotation. All candidates should be willing to work both independently and managing their teams effectively. Work prioritization, organizational skills, effective communication, and the ability to react quickly are critical to being successful. In addition to strong knowledge in Data Centers, hardware and network repair, candidates will create documentation, innovate solutions, participate in site-level project work, and fix complex problems within the Data Center Space.

Basic qualifications

• Bachelor's degree, or 4+ years of direct people management, including performance management and employee development experience
• Experience deploying, managing, and optimizing Microsoft Windows Server, or experience in Linux OS and network troubleshooting and experience that includes strong analytical skills, attention to detail, and effective communication abilities
• In-depth knowledge and experience of change / incident management.
• Experience working with customers with a passion for delivering exceptional service, or experience in web security and experience that includes strong analytical skills, attention to detail, and effective communication abilities.
• Experience in project management including resource planning, managing schedules, reporting project status and prioritizing tasks

Preferred qualifications

• Experience working in large-scale networking environments
• Experience in networking, storage systems, operating systems and hands-on systems engineering
• Data Center related certification - CDCP/S/E, CDCMP/TP is an added advantage

Job Purpose

Monitoring, validation and analyses of cross asset derivatives and listed markets. On boarding OTC and listed market data pricing into ICE Data Services systems and downstream products.

Responsibilities

• Monitor and validate cross-asset raw and derived market data using in-house analytical tools to ensure accuracy and integrity.
• Provide accurate, real-time, and historical data responses to customer queries, addressing data-related concerns promptly.
• Collaborate closely with internal teams, including operations and support, to streamline processes and resolve data issues.
• Perform data quality checks and facilitate data uploads to maintain high standards of information accuracy.
• Analyze cross-asset market data using tools such as Excel, SQL, and in house analytical tools to generate insights and reports.
• Identify and troubleshoot inconsistencies in data, ensuring timely resolutions.
• Stay updated with global financial news to monitor market trends and their impact on data.

Knowledge and Experience

• BA/BSc in Business, Finance, Statistics, Economics or Financial Engineering.
• Proficient in Excel
• Experience with SQL - advantage  
• Strong interest and familiarity with financial markets
• Team player, Independent and a fast learner
• Ability to work under pressure.
• Highly organized and detail-oriented.

Data Centre Delivery Services Engineer to lead and support the planning, design, and implementation coordination of data center infrastructure.

The ideal candidate will have at least 5 years of hands-on experience in interpreting DC plans, designing layouts, reviewing vendor submissions, and managing infrastructure implementation in line with technical and operational standards.

The person will be an independent contributor & work with different teams from the client (NW Team , Compute / Server team etc).

Responsibilities

Read, interpret, and validate data centre floor plans and technical drawings

Design and plan DC hall layouts for newly allocated spaces, including rack positioning, structured cabling, power, and cooling design

Review and assess vendor-submitted data centre plans, evaluate proposed technologies, and clarify design justifications

Present findings and proposals to internal stakeholders through clear reports and presentations

Coordinate and oversee the implementation of data centre infrastructure with vendors, contractors, and internal DCO teams

Ensure that implementation follows approved designs and complies with safety, regulatory, and operational standards

Contribute to the improvement of internal design standards and delivery practices

Requirements

Bachelor’s degree in Electrical Engineering, Mechanical Engineering, Computer Engineering, or related field

Minimum 5 years of experience in data center design, planning, and implementation coordination

Proven ability to interpret and work with data center schematics and technical layouts

Strong knowledge of rack layout, structured cabling, power distribution, and cooling infrastructure

Experience working with vendors, contractors, and internal engineering teams in infrastructure project delivery

Good communication and stakeholder management skills

Familiarity with regulatory and industry standards in high-availability data centre environments

Background in banking or other mission-critical facilities is preferred

Relevant certifications are highly regarded:

o CDCDP® – Certified Data Centre Design Professional

o DCDC – Data Centre Design Consultant

o ATD – Accredited Tier Designer

o CDCE® – Certified Data Centre Expert

o CDCMP® – Certified Data Centre Management Professional

o PE (Professional Engineer) certification is an added advantage, especially for validating and endorsing design compliance

• Extracting, analysing, and presenting data to support the Commercial team in meetings with key partners, including suppliers, buyers, and airlines.
• Involve in commercial negotiations, providing insights to drive business growth and optimize partner relationships.

Data Analysis & Interpretation:


• Analyze numerical data to identify trends, performance metrics, pricing insights, revenue opportunities

Commercial Support:


• Prepare and present detailed reports, dashboards, and summaries to the Commercial team ahead of and during meetings with partners to aid negotiation, partnership growth, and contract renewals.

Partner Performance Monitoring:


• Track and evaluate supplier/buyer/airline performance against KPIs, SLA adherence, and market benchmarks, highlighting areas for improvement or expansion.

Collaboration:


• Work closely with cross-functional teams including Sales, Product, Finance, and Operations to ensure accuracy, consistency, and relevance of shared data.

Ad-hoc Analysis & Insights:


• Provide quick, ad-hoc data analysis to support urgent commercial decisions or address partner inquiries

Job Requirements

• Degree holder
• Mandarin speaking to communicate with Chinese stakeholders
• Experience working in the Travel sector
• Ability to translate complex data into clear, concise insights and presentations for non-technical stakeholders.
• Basic understanding of the travel industry, commercial partnerships, and airline/supplier dynamics is a plus
• Comfortable presenting data and business insights in client-facing scenarios.
• 5 days work (Mon - Fri, 9am to 6pm) at Tanjong Pagar
• Salary $5000 - $7000

Interested applicants please send in your resume. Shortlisted candidates will be contacted very soon. Thank you.

Job Description:

• Specializing in data migration and system integration.
• The role involves designing, developing, and optimizing data migration solutions, ensuring seamless integration between business systems and technology platforms.
• Work closely with stakeholders to translate business requirements into scalable technical solutions.

Key Responsibilities:

• Design, develop, and maintain data migration scripts and workflows to support system implementations and upgrades.
• Develop and optimize SQL-based solutions for data extraction, transformation, and loading (ETL).
• Collaborate with business stakeholders to gather requirements and translate them into technical specifications.
• Perform data analysis, validation, and reconciliation to ensure data accuracy and integrity.
• Support system integration activities between SAP and other enterprise applications.
• Troubleshoot and debug issues related to data migration and system performance.
• Contribute to system design and propose improvements for data architecture and processing efficiency.
• Work closely with cross-functional teams to support deployment and implementation activities.
• Ensure documentation of technical designs, data mapping, and migration processes.

Requirements:

• Experience in data migration and data conversion projects.
• Strong proficiency in SQL scripting and data manipulation.
• Exposure to SAP debugging and data structures.
• Basic understanding of software development lifecycle (SDLC).
• Experience in system integration and data flow design.
• Strong analytical and problem-solving skills.
• Ability to engage stakeholders and translate business needs into technical solutions.
• Experience in healthcare systems is an added advantage.

Nice to Have:

• Experience with ETL tools or data integration platforms.
• Knowledge of data architecture and database design principles.
• Familiarity with cloud or enterprise systems.

Responsibilities:

• End-to-End Project Delivery: Lead data-driven initiatives from initiation to closure, including the implementation of Data Warehouses, Data Lakes, and visualization platforms.
• Stakeholder Management: Bridge the gap between technical teams (data scientists, AI engineers) and business units (actuaries, underwriters, and claims specialists).
• Data Governance & Compliance: Ensure all data projects adhere to strict industry regulations (e.g., GDPR, financial service standards) and maintain high data quality and security.
• Strategic Insights: Coordinate with data teams to leverage big data for actionable insights that drive underwriting efficiency and process optimization.
• Technical Planning: Define project scope, manage RAID logs (Risks, Assumptions, Issues, Dependencies), and oversee technical specifications and system workflows. 

Requirement:

• Bachelor’s or Master’s degree in Computer Science, Data Science, or Business Management.
• Certifications: PMP (Project Management Professional) or Agile/Scrum certifications are highly preferred.
• Technical Stack: Proficiency in languages like SQL, Python, or R and familiarity with tools such as Informatica, AWS, Azure, Tableau, or Power BI.
• Industry Knowledge: Understanding of insurance processes like Life or General Insurance underwriting, claims management, and policy.

Interested candidates, who wish to apply for the above position, please send in your resume to mail to mohamed.basith@persolapac.com

We regret that only shortlisted candidates will be contacted.

EA License No. 01C4394
EA Personnel Reg.No. R1104824 (Samsudeen Mohamed Basith)

This is in partnership with Employment and Employability Institute Pte Ltd (“e2i”). e2i is the empowering network for workers and employers seeking employment and employability solutions. e2i serves as a bridge between workers and employers, connecting with workers to offer job security through job-matching, career guidance and skills upgrading services, and partnering employers to address their manpower needs through recruitment, training, and job redesign solutions. e2i is a tripartite initiative of the National Trades Union Congress set up to support nation-wide manpower and skills upgrading initiatives. By applying for this role, you consent to e2i’s PDPA.
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