Principal Data Engineer

As the Principal Data Engineer, you’ll own all data platforms while staying hands-on in the code. You’ll work alongside Engineering and AI/Data Science, lead and develop the data team and turn business needs into a realistic plan.

• Own the data platforms: Build and run ingestion, orchestration, the BigQuery warehouse, DBT tooling and Python data services on GCP, including CI/CD, infrastructure as code, access and cloud costs.
• Keep data reliable: Set freshness and recovery targets for critical data, own alerting and incident response, and make backfills and reruns safe.
• Model data for the business: Design layered warehouse models, from staging to dimensional marts.
• Partner across teams: Work alongside Engineering and AI/Data Science to provide the data they need, and fix data issues at source.
• Plan the work: Turn business and analytics requests into one prioritised backlog and roadmap.
• Set the engineering standard: Review SQL, Python and dbt code, keep tests and contracts strong and make every production change testable and reversible.
• Lead and develop the team: Line-manage and mentor members of the data team, through regular 1:1s, coaching, code review and career development.

• Strong SQL and Python, building tested, maintainable production pipelines and data services.
• Hands-on experience with a cloud data warehouse, dbt (or equivalent) and workflow orchestration, including incremental loading, safe reruns and performance tuning.
• Data modelling for analytics and BI: dimensional design, semantic layers and well-documented shared datasets.
• Ownership of a production data platform on a major cloud: deployment, infrastructure as code, permissions, monitoring, incident recovery and cost.
• Sound security and data privacy practice, including GDPR and least-privilege access.
• Turning stakeholder needs into a prioritised backlog, and reconciling figures where finance reporting depends on them.
• Line management or mentoring of engineers, and technical leadership through code review and clear documentation.
• Experience with modern cloud data platforms, orchestration tooling, infrastructure as code and analytics platforms.

Desirables

• GCP experience with BigQuery, Composer and Cloud Run or Cloud Functions, including cost optimisation.
• Airbyte or similar ingestion tools, CDC, Kubernetes and data observability tools such as Elementary.
• Experience working with data scientists to put ML models or pipelines into production.

• Competitive salary
• Competitive annual leave allowance
• Private medical insurance
• Flexible working arrangements