Set the technical direction for pipeline architecture and data quality standards
Take end-to-end ownership of mission-critical pipelines — from raw ingestion through to clean, documented delivery in BigQuery
Be the go-to technical authority on data behaviour across the platform
Design and develop data processing, cleansing, transformation, and QA scripts using Python and SQL
Own the operation, maintenance, and enhancement of existing mission-critical data pipelines and ETLs
Monitor pipeline reliability and data quality standards; identify and close coverage gaps systematically
Optimise ETL performance and migrate legacy pipelines into a more scalable architecture
Define and enforce data quality standards across the full pipeline
Contribute to architectural decisions on ingestion, transformation, and delivery layer design
Own the full data lifecycle documentation: every transformation rule, edge case, and business logic decision must be spec'd
Fetch datasets from external data sources: REST APIs, JSON, CSV
Perform data migrations and batch data updates
Conduct exploratory analysis to support new business requirements and data source onboarding
Develop proof-of-concept pipelines for new data initiatives

Technical Graduate in Computer Science, Information Systems, Statistics, Mathematics, or related field
7+ years of relevant experience in data engineering, database or ETL development
Excellent Python skills (pandas, numpy); able to write production-grade pipeline code
Excellent SQL skills and strong database design and development experience
Proven ETL development and long-term maintenance experience
Apache Airflow experience
Entity resolution and deduplication across heterogeneous, multi-source datasets
Web scraping, crawler development, and API integration experience
Working knowledge of AI/LLM tooling (Claude Code, Copilot, or equivalent)

Senior Data Engineer

Key skills