Data Technical Team Lead

Gic; Pretoria; On-site; ZAR 1,200,000 - 1,800,000; Full time

Job summary

GIC is seeking a Data Technical Team Lead to steer the enterprise data platform from architecture to implementation in Pretoria. The role blends strategic leadership with hands-on engineering in SQL Server and PostgreSQL, building data lakes, data warehouses, and data lakehouse architectures to support analytics and governance.

You will lead Data Engineering, Data Science, and BI functions, drive standards, scale data services, and ensure secure, auditable data flows across the organisation.

Qualifications

• 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 PostgreSQL production experience.

Responsibilities

• Lead and mentor the Data Engineering, Data Science and BI teams.
• Design and implement enterprise data strategy and scalable architecture.
• Build and operationalise data lake/data warehouse/lakehouse platforms.
• Govern data quality, security, and compliance across subsidiaries.

Skills

Advanced SQL

PostgreSQL

Data modelling

Data warehouse design

Data lake / lakehouse architecture

ETL/ELT pipelines

SQL Server

Education

Bachelor’s Degree in Computer Science or equivalent

Tools

SQL Server

PostgreSQL

Job description

Job description

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 Specification

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

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