VP, Software Engineer (Data)

Singapore Exchange Limited; Singapore; On-site; SGD 260,000 - 380,000; Full time

Job summary

Singapore Exchange Limited is seeking a senior software and data engineering leader to translate enterprise data strategy into actionable plans, drive the evolution of the data platform, and mentor engineers to deliver scalable applications, APIs and data services.

You will champion security-by-design, governance, and reliability across the SDLC, partnering with stakeholders to ensure data products are discoverable, trusted, and compliant while enabling analytics and AI workloads.

Responsibilities

• Lead development of complex applications, services and APIs across the data domain.
• Establish coding standards and mentor engineers across user-facing products and data services.
• Oversee data platform extensions, data-as-a-service offerings and data agents.
• Lead design of major systems and data architectures across application, API, storage and processing layers.
• Set direction for how data products are built and operated with curated, contract-backed datasets.
• Drive security-by-design practices for sensitive market and client data.
• Partner with data governance to surface lineage, entitlements and privacy controls.
• Lead integration design across data platform, trading, risk and enterprise systems.
• Improve SDLC, automation and engineering standards across data assets.
• Lead reliability improvements through telemetry, monitoring and incident leadership.
• Mentor engineering teams and foster talent across software and data engineering.

Job description

• 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.