Total 1,005 data entry job vacancies in Information Technology category

We are seeking an experienced Data Strategy Leader to design, implement, and drive an enterprise-wide data strategy within an SME environment. This role requires a unique balance of strategic leadership, hands-on execution, and stakeholder management, ideally within the Information or Telecommunications domain.

The successful candidate will embed data-firstprinciples across business and technology teams, establish scalableenterprise data platforms, and lead cross-functional teams to delivermeasurable business outcomes.

Data Strategy & Transformation

• Lead the enterprise-wide data strategy, embedding data-first principles across business, product, and technology teams.
• Translate business objectives into a practical, execution-ready data roadmap suitable for an SME operating environment.
• Define and govern enterprise data standards, policies, and operating models aligned to growth and scalability needs.

Data Platform & Architecture

• Design and deploy a scalable enterprise data lake platform, consolidating data across multiple systems and domains.
• Provide hands-on technical leadership in defining enterprise data architecture, models, and integration frameworks.
• Ensure platforms support advanced analytics, reporting, and future AI/ML readiness.

Stakeholder & Adoption Management

• Drive organisation-wide adoption of data capabilities, aligning stakeholders from executive leadership through to delivery teams.
• Act as a key bridge between business SMEs, data engineers, analytics teams, and IT leadership.
• Lead change management initiatives to embed data-driven decision-making into day-to-day operations.

Governance, Quality & Stewardship

• Implement data stewardship and ownership models to improve data quality, accountability, and trust.
• Establish clear data governance processes pragmatic for an SME context (not overly bureaucratic).

Delivery, Execution & Leadership

• Build and lead cross-functional data task forces, bringing together domain SMEs and data experts to accelerate execution.
• Define KPIs and success metrics to track data strategy impact, value creation, and continuous improvement.
• Maintain a pragmatic, delivery-focused approach, balancing long-term strategy with near-term execution.

Required Experience & Skills

• Demonstrated experience leading data strategy initiatives in an SME environment, including hands-on delivery.
• Proven track record of rolling out an enterprise-wide data strategy programme end-to-end.
• Strong stakeholder management skills, with experience influencing senior leadership and cross-functional teams.
• Hands-on experience with enterprise data platforms, data lakes, and modern data architectures.
• Ability to operate at both strategic and technical levels.

• Background in Information, Telecommunications, or adjacent data-intensive industries is strongly preferred.

About the Role

Looking to build a career where data directly drives business decisions? This role offers a unique opportunity to work at the intersection of data analytics and B2B marketing, where your insights will actively shape sales strategy and market growth.

You won’t just maintain data — you’ll transform it into actionable intelligence that influences real business outcomes from day one.

Key Responsibilities

Data Analysis & Strategy

• Develop and manage datasets to support sales analysis and market development
• Translate data insights into clear recommendations for sales direction
• Work with tools such as Microsoft Excel, SAP, Power BI, and internal systems

Marketing & Lead Generation

• Support B2B marketing initiatives, including campaigns and promotions
• Contribute to lead generation strategies and pipeline development
• Collaborate with sales teams to align data insights with business goals

What We’re Looking For

Technical Skills

• Strong proficiency in Microsoft Excel (Modern Excel preferred)
• Exposure to Power BI / Power Automate is advantageous

Mindset & Traits

• Proactive and confident in proposing ideas to improve performance
• Analytical and curious — always asking “why” behind the data
• Detail-oriented with strong commitment to accuracy and quality
• Able to act quickly and take ownership of tasks
• Service-oriented mindset with strong stakeholder awareness

Requirements

• Bachelor’s Degree in any discipline

Why Join Us?

Empowered Culture

• Flat structure where ideas are valued regardless of seniority
• Opportunity to make visible impact on business performance

Growth Exposure

• Hands-on experience across data analytics, marketing, and sales strategy
• Ideal for candidates looking to build a well-rounded commercial skillset

Business Edge Personnel Services Singapore 

EA License No: 96C4864 

EA Personnel Reg No: R26159908 (Tan Ying Ying)

• Plan and execute marketing initiatives for AI data center and immersion cooling solutions.
• Create professional content: presentations, solution briefs, case studies, and digital/LinkedIn materials.
• Drive local campaigns, webinars, and events to generate leads and strengthen client engagement.
• Collaborate closely with sales and technical teams to develop customer-facing materials and proposals.
• Represent the company professionally at client meetings and industry events.

Requirements:

• Degree in Engineering, IT, Marketing, Business, or equivalent
• At least 5 years of B2B marketing experience in data centers, IT infrastructure, AI/GPU platforms, or cooling solutions.
• Solid understanding of data center operations, rack layouts, cooling technologies, and GPU-based AI systems.
• Ability to translate technical concepts into clear value propositions for non-technical stakeholders.

If you are keen to apply for the position, kindly email your detailed resume in MS Word to hr@recruit-expert.com

Please note that only shortlisted candidates will be notified.

For more job opportunities, please visit our website at www.recruit-expert.com

EA Licence: 19C9701

Registration: R1326740

Job Description:

• Responsible for analyzing the organization and designing its processes and systems.
• Assess the business model and its integration with technology, gather and synthesize information, and identify customer requirements.
• Define the future state and/or business solution.
• Expected to be an SME, collaborate and manage the team to perform, and make team decisions.
• Engage with multiple teams and contribute to key decisions.
• Provide solutions to problems for your immediate team and across multiple teams.

Requirements:

• Experience in Data Migration and conversion of data.
• Good in SQL scripts.
• Basic SAP Debugging.
• Healthcare industry knowledge will be advantageous.
• Good in Stakeholder handling.
• Business Process Analysis Tools proficiency is required.
• Business Model Strategy, Business Process Design, and Requirements Analysis proficiency are recommended.

We are seeking an experienced Senior Data Engineer to design, build, and optimize scalable data platforms that support analytics, reporting, and data-driven decision-making. The successful candidate will work closely with data scientists, analysts, and application teams to ensure reliable, secure, and high-performing data solutions across the organisation.

Key Responsibilities

• Design, develop, and maintain robust data pipelines and ETL/ELT workflows for structured and unstructured data
• Architect and manage data warehouses, data lakes, and data marts to support analytics and reporting needs
• Optimize data models for performance, scalability, and reliability
• Ensure data quality, integrity, governance, and security standards are met
• Collaborate with business stakeholders, data scientists, and analysts to translate requirements into technical solutions
• Lead troubleshooting and performance tuning of data pipelines and platforms
• Implement monitoring, logging, and alerting for data workflows
• Mentor junior data engineers and provide technical guidance on best practices
• Contribute to architecture discussions, technical standards, and documentation

Requirements

• Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related field
• Minimum 5–8 years of hands-on experience in data engineering or related roles
• Strong proficiency in SQL and data modelling
• Solid experience with ETL/ELT frameworks and data orchestration tools
• Proven experience building and maintaining large-scale data platforms
• Strong analytical, problem-solving, and communication skills
• Ability to work independently and collaborate within cross-functional teams

Technical Skills (Preferred)

• Programming experience in Python, Java, or Scala
• Hands-on experience with cloud platforms (AWS, Azure, or GCP)
• Experience with data warehousing technologies (e.g. Snowflake, BigQuery, Redshift)
• Familiarity with big data technologies (e.g. Spark, Hadoop, Kafka)
• Experience with CI/CD, version control (Git), and Infrastructure as Code (IaC)
• Knowledge of data governance, security, and compliance standards
• Experience working in Agile / DevOps environments

Nice to Have

• Exposure to data visualization or BI tools (e.g. Power BI, Tableau)
• Experience supporting machine learning or advanced analytics workloads
• Prior experience in regulated or large enterprise environments

Work location: Downtown
Working hours: Monday to Friday office hours
Salary: up to $4500 per month

3 months contract (subject to extension)

Job Scope:

• Digital Operations Management:
• Execute daily website content uploads, updates, and maintenance
• Process content update requests from internal teams and external stakeholders
• Coordinate event approval workflows ensuring compliance with established criteria and standards
• Conduct comprehensive quality assurance checks on all files and content before publication
• Monitor published content for accuracy, consistency, and currency of information
• Analytics and Performance Reporting:
• Extract and compile key performance metrics for weekly target performance reports with accurate data delivery to stakeholders
• Support trend analysis to identify patterns and insights from performance datasets
• Collaborate with marketing teams to put together reports for campaign performance and funnel analysis

Requirements:

• Minimum Diploma in any field
• At least 3 years of experience in related fields
• Basic data analytics capabilities
• Proficiency in spreadsheet software (Excel, Google Sheets) and CSV file management
• Strong written and verbal communication skills for effective stakeholder coordination
• Meticulous attention to detail for quality assurance and data accuracy
• Adaptability and learning agility to master new digital tools and evolving requirements
• Problem-solving skills for troubleshooting content and data inconsistencies

Interested applicants please apply or email your CV to nadirah.hirwandy@tg-hr.com

SINGAPOREANS ONLY

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.

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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