Director, Lead Data Platform Engineer

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Division: Ancillary & Support Services (ANSS)

JOB PURPOSE

The Director, Lead Data Platform Engineer owns the design and delivery of a unified data platform for the Group. The role connects data held in separate systems across schools, campuses and corporate functions, creating reliable, business-ready information for domain teams, applications and the Forward Deployed Engineering team.

The core deliverable is a governed lakehouse with Bronze, Silver and Gold layers. Bronze retains source data and history; Silver validates and integrates priority sources into a connected model; and Gold provides curated business entities and data products. The Gold layer and shared semantic model must also give AI agents dependable, appropriately governed data. This is a senior hands‑on role that remains close to the build while setting architecture and engineering standards.

RESPONSIBILITIES

1. Architect and Deliver the Data Platform

• Own the target lakehouse architecture, including storage, compute, medallion layers, data modelling and platform standards.
• Define source ingestion patterns, conformed dimensions, master and reference data, and a shared semantic model that connects databases in the Silver layer.
• Create governed Gold business entities and data products that domain teams and applications can use consistently.
• Build and oversee incremental ELT/ETL pipelines from Bronze through Silver to Gold, integrating relational, application and operational sources in order of business value.
• Manage performance, reliability, observability and cost so the platform works effectively in production.

2. Establish a Foundation for Agentic AI

• Design the Gold layer and semantic model with clearly defined business entities and dependable data freshness so AI agents can use trusted context.
• Develop retrieval foundations where needed, including vector stores, embeddings, metadata and governed access interfaces such as APIs or MCP‑style tools.
• Apply access controls, data contracts, lineage and audit trails so AI systems use only authorised data and access remains traceable.

3. Govern and Secure Group Data

• Establish data quality standards, cataloguing, lineage, master data management and clear ownership across business domains.
• Embed privacy and security controls for learner and staff data throughout ingestion, modelling, access and operations.

4. Lead Engineering and Business Adoption

• Set Group data engineering and modelling practices and mentor engineers and analysts who build on the platform.
• Partner with business‑domain owners and the Forward Deployed Engineering team so Gold becomes the trusted source for new solutions.
• Own the platform roadmap and advise the CTO on delivery sequence, technology choices and build‑versus‑buy decisions.

MINIMUM ACADEMIC/PROFESSIONAL QUALIFICATION

Bachelor’s degree in Computer Science, Data Engineering, Software Engineering, Information Systems or a related discipline from a recognised institution; equivalent qualifications and substantial relevant experience may be considered.

• A postgraduate qualification in data engineering, computer science, analytics or a related field is an advantage.
• Relevant professional certification in a major cloud or data platform, such as Microsoft Azure / Fabric, Databricks, Snowflake, AWS or Google Cloud, is an added advantage.
• Minimum 10 years of relevant experience in data engineering, data architecture or enterprise data platform delivery.
• Within this experience, at least 5 years designing and delivering production data platforms and at least 3 years in technical leadership, including setting engineering standards, mentoring teams and partnering with business stakeholders.

RELATED EXPERIENCE

• Proven experience architecting and delivering a lakehouse or medallion platform into production.
• Deep data modelling experience, including dimensional, data‑vault or equivalent methods and conformed models across multiple source systems.
• Hands‑on experience building ELT/ETL pipelines and integrating diverse databases and APIs at scale.
• Experience with a modern cloud data platform such as Microsoft Fabric / OneLake, Databricks, Snowflake or an equivalent, and open table formats such as Delta, Iceberg or Parquet.
• Experience setting technical standards, mentoring engineers and working credibly with business stakeholders.
• Experience delivering a Group‑wide data platform, building data foundations for AI systems, or working in education or another multi‑entity setting is advantageous.
• Strong SQL and a data engineering language such as Python, PySpark or Scala; practical command of transformation frameworks such as Spark, dbt or platform equivalents.
• Knowledge of medallion architecture, open table formats, relational and NoSQL sources, master and reference data, and semantic modelling.
• Sound understanding of data quality, lineage, cataloguing, governance, security and privacy, especially for sensitive learner and staff data.
• Ability to design cloud data workloads across storage, compute, networking, identity and access, with attention to scaling, reliability and cost.
• Experience with Git‑based development, testing, CI/CD, orchestration, infrastructure as code and monitoring for production data pipelines.
• Ability to translate business needs into a sequenced roadmap and make clear architecture and build‑versus‑buy recommendations.
• Knowledge of vector stores, embeddings, retrieval‑augmented generation, governed APIs or MCP‑style access, streaming and AI feature serving is advantageous.

REPORTING & STRUCTURE

Reports to: Chief Technology Officer (CTO)

Location: Southeast Asia, within the Group technology function | Employment type: Full‑time

Works closely with: Forward Deployed Engineering team, business‑domain owners, IT and security teams.

Team structure and direct reports: to be confirmed.

SUCCESS MEASURES

• First 90 days: agree the target architecture and delivery roadmap, establish the platform foundation and ingest the first priority sources into Bronze and Silver.
• First year: connect key Group databases in a working Silver layer; make the first Gold business entities available to real consumers; and establish governance and security controls.
• Ongoing: make Gold the trusted default source across the Group, measured by adoption, data quality and time to integrate new use cases.