Data Engineer

Role Purpose

Independently design, develop, maintain and improve reliable data pipelines, transformations and analytical data structures that support enterprise analytics across the FEU Group. The role owns assigned engineering deliverables end-to-end within established architecture and standards. It reports administratively and for performance management to the Head, Data Governance & Analytics, while working with the Manager - Data Engineering for functional and technical guidance. Major architectural or cross-platform decisions are escalated through the appropriate functional and Head-level governance.

Key Accountabilities

• Design, develop, test, deploy and maintain ETL/ELT pipelines and integrations across enterprise data sources.
• Develop advanced SQL transformations and strong Python-based data processing and automation solutions.
• Develop and maintain dimensional and analytical data models; translate analytical requirements into appropriate structures.
• Profile source data; implement validation, reconciliation, data-quality controls and exception handling.
• Troubleshoot pipeline failures, data anomalies and performance issues; perform root-cause analysis and implement sustainable fixes.
• Apply version control, peer review, testing, logging, monitoring and documentation practices; build reusable rather than one-off solutions.
• Maintain technical metadata, transformation logic and lineage; implement approved governance definitions in engineering solutions.
• Collaborate independently with Analytics, IT Services and domain SMEs on assigned requirements and production support.

Minimum Qualifications & Experience

• Bachelor's degree in Computer Science, Information Systems, Data Science, Engineering, Mathematics, Statistics or related discipline.
• Minimum 3 years relevant experience in data engineering, database development, ETL/ELT or related work.
• Advanced SQL and strong Python proficiency.
• Strong relational database and ETL/ELT fundamentals; working knowledge of dimensional/data modeling.
• Experience integrating multiple data sources and using Git/version control.
• Cloud data platforms, Microsoft Fabric/Azure, APIs, orchestration, CI/CD, R and automated quality frameworks are advantageous.