The role
We are interested in data engineers who enjoy making data dependable from ingestion to consumption. You would build and improve pipelines, define quality checks with data users and make lineage and failures easier to understand. The role calls for hands-on engineering and careful collaboration with analysts, application teams and platform colleagues. You would help data users understand the limits of each dataset. This is a defined role for ongoing recruitment; project context would be confirmed with you if an opportunity is available.
What you would work on
- Develop Python and SQL transformations that ingest, validate and prepare structured or semi-structured data for agreed downstream uses.
- Design pipeline schedules, dependencies and recovery paths so a failed load is visible, diagnosable and safe to rerun.
- Work with data consumers to define schemas, freshness expectations and quality rules, then automate checks for missing, late or inconsistent records.
- Document source-to-target mappings, ownership and lineage; protect sensitive information through appropriate access and retention practices.
- Improve pipeline performance and cost by measuring bottlenecks, testing changes and keeping operational instructions current.
Essential experience
- Hands-on Python and SQL development for repeatable data processing, including joins, incremental loads and transformation testing.
- Practical understanding of data modelling, pipeline orchestration, data quality and how to investigate a discrepancy back to its source.
- Ability to collaborate with people who use the data and explain clearly what a dataset can and cannot support.
Useful additional experience
- Cloud data services, streaming ingestion, infrastructure automation or governance controls for shared analytical data.
- Experience preparing trustworthy data for machine-learning or AI applications without assuming model development is the core of this role.
What to include in your application
Send your résumé and describe a pipeline you built or improved. Explain the source, transformation, data-quality checks, failure handling and how consumers used the result. Include a case where the data was wrong or late and how you found the cause and restored confidence. If you have a sanitized data model or code sample, you may include it. Please select the Data Engineer posting so your résumé is associated with this role.