VP, Software Engineer (Data) Greater Singapore Area

BUILD MARKETS. SHAPE ECONOMIES.

At SGX Group, we create markets. We turn ideas into products and expand access to new opportunities. We are building the future of the exchange in-house: from architecture and platforms to the critical systems that power markets. The biggest decisions are still open to people who join us now.The opportunity

As Vice President, Software Engineer (Data), you will lead engineering across the data domain, spanning the applications, data platforms and data products that SGX Group's business and market infrastructure depends on. You will set direction, strengthen engineering discipline and take accountability for how data services, and the applications and interfaces built on top of them, are designed, delivered, operated and improved.

The challenge is to increase agility without compromising resilience, security, governance or operational reliability. In a mission-critical environment where data carries regulatory, client and market obligations, engineering decisions have real consequences, and you will need to make sound trade-offs across architecture, delivery, risk and business priorities.

There are strong foundations to build on, and a significant amount still to do. The engineering model, the data platform, tooling and ways of working will keep changing. If you want to inherit a mature data engineering organisation, this is not that one yet. It will suit someone who enjoys complexity, is comfortable leading through change and wants to build capability that lasts.The Data Engineering Team

This role performs software engineering within the Data Engineering team that builds, runs and evolves the applications, data platforms and data products behind SGX Group’s business and market infrastructure.The team works closely with product, architecture, platform, data governance, analytics, security and delivery colleagues to turn business priorities into secure, resilient and scalable data services. Its remit spans the full engineering lifecycle, from technical design, build, data modelling, CICD, testing etc through to production ownership of pipelines and continuous improvement.

Our next phase is about strengthening engineering ownership, modernising our data platform and practices, treating data as a product, and building consistency across teams without flattening the different needs of each business domain.What you will do

Software Development: Lead development of complex applications, services and APIs across the data domain, establish coding standards and mentor engineers, including the user-facing products that extend the data platform, data-as-a-service services and data agents etc.

Software Design: Lead design of major systems and data architectures and influence cross-domain technical design, spanning application, API, storage, processing and data serving layers.

Data Product Engineering: Set the direction for how data products are built and operated — curated, documented, contract-backed datasets and data services — and establish the standards that make them discoverable, trusted and usable by consumers across the organisation and beyond.

Data Platform Engineering: Lead the evolution of shared data platform capabilities spanning ingestion, storage, processing, orchestration, cataloguing and self-service tooling, so teams across SGX Group can deliver data capabilities safely and at speed.

Data Pipeline Development: Lead the design of production-grade batch and streaming pipelines and establish the quality, performance and security controls applied across them.

Data Quality & Reliability: Establish quality engineering practices for both applications and data, drive defect prevention initiatives, and define service levels for freshness, completeness and accuracy.

Secure Software & Data Development: Drive security-by-design practices across teams and ensure sensitive market, client and reference data is handled in line with standards.

Data Governance & Compliance: Partner with data governance to set lineage, cataloguing, entitlements, access control, retention and privacy practices in line with regulatory and internal obligations, and ensure they are surfaced through the tooling that data users interact with.

System & Data Integration: Lead integration design across the data platform and trading, clearing, data services, market operations, risk and enterprise systems.

SDLC & Delivery Excellence: Lead adoption of SDLC best practices, automation and engineering standards, applying the same engineering rigour to data assets as to application code.

Observability & Production Support: Lead reliability improvements across applications, pipelines and data services through telemetry, monitoring and operational practices, including incident leadership.

Automation & Productivity: Lead engineering productivity improvements through platform and workflow optimisation, including self-service capabilities that reduce manual handling of data requests.

Leadership & Mentoring: Lead engineering communities and develop technical talent across software and data engineering practice.

Agentic AI Engineering: Lead the adoption of agentic AI engineering practices in the domain, define usage patterns and guardrails, mentor engineers on agent orchestration, and improve shared agent workflows so recurring defects, review gaps and testing weaknesses are addressed through reusable automation. Set direction for the governed data foundations that make analytics and AI workloads viable.

Translate enterprise, technology and data strategy into actionable engineering plans, partnering closely with project and programme leaders to align priorities, resources, delivery milestones, and management reporting.

Lead engineering, data and architecture governance forums, and engage senior stakeholders, including internal and external data consumers, on strategic priorities, trade-offs, delivery progress, and investment decisions.

What we are looking for

Experience: Leadership experience in software and data engineering, with accountability for delivery across multiple teams, platforms or business domains within complex enterprise environments.

Track record: Track record: Demonstrated success in defining and executing technical roadmaps, reducing technical debt and evolving platforms while balancing delivery commitments, operational risk and business priorities.

Technical stack: Strong knowledge of modern software and data landscapes and platform practices, including microservices, event-driven systems, API design, resiliency patterns, Kubernetes, PagerDuty, CI/CD, modern observability tools and infrastructure as code; together with SQL and languages and frameworks such as Python, Java, TypeScript or KDB+; streaming technologies such as Kafka; orchestration and transformation tooling such as Airflow, Dataform/dbt or equivalents; cloud and data platforms such as GCP/KDB+/Snowflake; and open table and file formats such as Iceberg, Delta or Parquet.

Software design: Deep software design fundamentals and the ability to set them as standards across teams, including system and component design, API and interface design, domain modelling, design patterns, concurrency, performance, testability and maintainability, applied to both applications and data systems.

Data craft: Strong grasp of data modelling, data contracts and schema evolution, data quality engineering, lineage, entitlements, and cost and performance tuning at scale, with the ability to establish these as practice across teams.

Standards & environment: Experience leading engineering teams in high-availability, security-sensitive or regulated environments, with strong capability in incident leadership, data governance and privacy obligations, and technical risk management.

Communication & leadership: Strong stakeholder management and communication skills with senior business and technology leaders, with the ability to explain technical and data concepts clearly to both engineering and business audiences, and demonstrated people leadership including team scaling, hiring, succession planning, coaching and performance management.

Education & certifications: Bachelor’s degree in Computer Science, Engineering, Information and Communications Technology, Data Science, or a related discipline.

Domain Knowledge: Exposure to financial market infrastructure, including securities and derivatives trading, clearing, settlement, market operations or exchange-related systems, and the data they generate such as market and reference data, trade and position data, and post-trade or regulatory reporting.

Why this role matters

You will work on the data that underpins critical market infrastructure, where engineering decisions have direct consequences for the business, its customers, its regulators and the markets it serves. Not many engineering roles carry that weight.

The mandate is real. You will shape how data platforms and data products are engineered, owned and evolved, and what data engineering becomes at SGX Group. The work is demanding and the organisation is still changing, which is exactly where the opportunity sits. There are not many chances in a career to build something that outlasts you.

About SGX Group

SGX Group is one of the world's most trusted international marketplaces, known for its stability and openness. Anchored in Singapore, we enable price discovery, capital formation and risk management across asset classes, supported by resilient infrastructure and robust clearing. We convene issuers, investors and intermediaries to create and grow markets that stand the test of time. Find out more at .