Data Engineer

Technologies we use

Expected

• SQL
• Python
• Terraform
• AWS Glue

About the project

Rebuild the data platform behind the UK’s leading automotive marketing products. You’ll own how campaign, vehicle, and dealership data flows through the platform, replacing manual processes with reliable, scalable solutions.

This is how we organize our work

This is how we work

• at the client's site
• you focus on a single project at a time
• you focus on product development

Team members

• backend developer
• big data developer
• data scientist
• product owner

Your responsibilities

• Re-engineer our marketing data pipeline using a modern analytics stack, designing ingestion, dbt-based transformations, and Redshift warehouse models to replace today’s legacy SQL Server-centric processing.
• Take ownership of our production database estate, including performance, integrity, security, and cost management. Introduce CI/CD for database development so schema changes are version-controlled, tested, and deployed like code.
• Build automated data-quality processes and eliminate manual data movement, including migrating legacy SFTP workflows to AWS Transfer Family, so routine data operations run reliably without manual intervention.
• Build the data side of automated client onboarding, creating repeatable ingestion templates, validation, and mapping processes so new automotive clients can go live in hours rather than weeks.
• Use AI tooling such as Claude on a daily basis for query optimisation, pipeline debugging, and documentation, leveraging AI to increase your impact across a large and complex data estate.

Our requirements

• 3–5 years of hands-on experience building data pipelines and/or managing production databases. You have owned data platforms end-to-end, rather than simply writing queries against existing systems.
• Expert-level SQL skills with production experience in SQL Server (T-SQL) and at least one columnar data warehouse such as Redshift, Snowflake, or BigQuery. You understand query execution plans, indexing strategies, and warehouse-specific optimisation techniques.
• Hands-on experience with dbt or similar data transformation frameworks, Python for data engineering, and orchestration/ETL tools such as AWS Glue, Airflow, or Step Functions.
• Comfortable working with Terraform, CloudFormation, or CDK, as well as CI/CD pipelines. You treat databases and data pipelines as version-controlled, tested, and deployable artefacts.
• Proven experience migrating data workloads between platforms. You know how to execute migrations safely, including parallel runs, data reconciliation and validation checks, and well-planned cutovers that ensure no data is lost.
• Comfortable working in English with UK-based stakeholders, gathering knowledge from existing system owners, and documenting data pipelines and processes so that others can operate and maintain them effectively.

Benefits

• sharing the costs of sports activities
• private medical care
• life insurance
• remote work opportunities
• flexible working time