Data Technical Team Lead

 The Data Technical Team Lead is responsible for the strategic and technical leadership of GIC’s Data Engineering, Data Science, and Data Analytics & BI functions.

This role will design, build, and lead the implementation of GIC’s enterprise data platform from the ground up — including data architecture, data structures, pipelines, governance, and analytics enablement. The incumbent must be hands-on and capable of architecting and implementing SQL/PostgreSQL-based solutions, while leading the development of a scalable Data Lake / Data Warehouse / Data Lakehouse architecture best suited to GIC’s operational and strategic needs.

The role combines:

• Strategic data leadership
• Deep technical architecture capability
• Hands-on engineering competence
• Team leadership across multiple data disciplines

1.  Job Spesification

 1.1 Data Strategy & Architecture

• Define and implement GIC’s enterprise data strategy.
• Design and build a scalable, secure, and future-proof: Data Lake, Data Warehouse and Data Lakehouse architecture (based on business requirements and cost model).

• Develop logical and physical data models across operational and analytical domains.
• Establish enterprise data standards (naming conventions, modelling standards, metadata, etc.).
• Implement master data management (MDM) principles where required.
• Ensure data platform alignment with cybersecurity and ISO27001 controls.

1.2 Data Platform Engineering (Hands-On)

• Architect and implement database environments using: SQL Server and/or PostgreSQL and Advanced SQL development (T-SQL / PL/pgSQL)
• Design and build: ETL / ELT pipelines, Data ingestion frameworks, Data transformation layers and Data orchestration processes
• Develop: Fact and dimension schemas (star/snowflake/databricks), Normalised operational data structures, Aggregated reporting structures
• Implement data partitioning, indexing, performance tuning and query optimisation.
• Establish backup, retention and disaster recovery strategies for data platforms.
• Ensure scalability, high availability and performance of the data environment.

1.3 Data Engineering Leadership

• Lead and mentor the Data Engineering function.
• Establish standards for: Code management, Version control, Data pipeline development, Testing and deployment (CI/CD where applicable)
• Oversee integration of: ERP systems, Financial systems, Project management systems and External data feeds
• Ensure reliable and auditable data flows across subsidiaries and offices.

1.4 Data Science Enablement

• Provide technical infrastructure and data readiness for Data Science initiatives.
• Ensure clean, structured, and feature-ready datasets.
• Support the deployment of predictive models into production.
• Enable advanced analytics use cases: Forecasting, Risk modelling, Asset performance analytics and Financial trend modelling
• Ensure compute environments support model development and deployment.

1.5 Data Analytics & BI Governance

• Lead the Data Analyst and BI function.
• Design and govern enterprise semantic models.
• Ensure a “single source of truth” across reports and dashboards.
• Define KPI governance frameworks for: Infrastructure performance, Financial metrics, Operational metrics, Executive reporting
• Oversee BI platform architecture (e.g., Power BI or equivalent).
• Ensure performance and data refresh optimisation.

1.6 Governance, Security & Compliance

• Implement data governance frameworks including: Data ownership, Data classification, Access controls and Audit trails
• Align with ISO27001 and corporate cybersecurity policies.
• Ensure role-based access to sensitive financial and infrastructure data.
• Maintain data quality monitoring and reporting.

1.7 Leadership & Management

• Lead and coordinate Data Engineers, Data Scientists and Data Analysts / BI Specialists
• Develop roadmaps and delivery plans aligned to GIC’s strategic objectives.
• Present data strategy and progress to Executive and Board level.
• Manage vendor relationships and technology evaluations.
• Develop budget proposals for data infrastructure investments.

2. Technical Requirements

2.1 Essential Technical Skills

• Advanced SQL (mandatory)
• Strong PostgreSQL experience (mandatory)
• Data modelling (OLTP and OLAP)
• Data warehouse design
• Data lake / lakehouse architecture design
• ETL/ELT pipeline development
• Query performance tuning and optimisation
• Indexing strategies
• Data Analytics & BI Governance
• Backup and disaster recovery planning
• Experience integrating multiple enterprise systems

3.  Minimum Qualifications & Experience

• Bachelor’s Degree in: Computer Science, Information Systems, Engineering or related technical field
• 8–10+ years in Data Engineering / Database Architecture
• 3–5+ years leading technical teams
• Proven experience building a data warehouse or data platform from scratch
• Demonstrated experience in PostgreSQL production environments

Contact information

Natasha