About this opportunity
Taylor's University Sdn Bhd lists this Director, Lead Data Platform Engineer opportunity in northern, Kentucky. Review the employer’s description below for duties, qualifications and application requirements.
Job description
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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.
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Who can apply
Review the original listing for work authorization, qualifications and employer requirements.