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# Senior Data Engineer

- **Company:** FDJ UNITED
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, Amazon S3, Application Frameworks, Cloud Computing, Cloud Database, Continuous Integration, Directed Acyclic Graph (Directed Graphs), Data Architecture, Information Engineering, Data Governance, Extract Transform Load (ETL), Cursor (Graphical User Interface Elements), Dimensional Modeling, Python (Programming Language), Meta-Data Management, SQL Databases, Data Streaming, Sql Optimization, Apache Spark, Data Lakes, Pyspark, Kubernetes, Apache Kafka, Terraform, Data Pipelines, Docker
- **Published:** July 18, 2026
- **Apply:** https://uk.indeed.com/viewjob?jk=cb9b9e86a1928844

## About the Role

Requires 5+ years of data engineering experience with strong expertise in SQL, CI/CD, data quality frameworks, and cloud technologies (S3, Athena, Redshift), with a plus for streaming and AI tooling experience., * 5+ years in data engineering

 * Strong Python, advanced SQL, Airflow, Spark/PySpark, dbt, and CI/CD proficiency
 * Experience with Lakehouse architectures, dimensional modeling, and cloud data warehouses (S3, Athena, Redshift)
 * Data quality frameworks, monitoring, and schema evolution expertise

Nice-to-Haves

 * Streaming platforms (Kafka, Kinesis)
 * AI coding assistants (Cursor, Claude Code, Copilot)
 * Terraform, Kubernetes/Docker
 * Data catalog/metadata management experience

We believe talent knows no boundaries. Our hiring process focuses solely on your skills, experience, and potential to contribute to our team. We welcome applicants from all backgrounds and evaluate each candidate based on merit, regardless of personal characteristics as the age, gender, origin, religion, sexual orientation, neurodiversity or disability.

## Description

* Pipeline Development: Build ETL/ELT pipelines (Python, SQL), optimize Spark/PySpark jobs, manage Airflow DAGs, and implement dbt transformations.
 * Lakehouse Architecture: Design medallion-style architectures using S3 and modern table formats (Iceberg, Delta Lake) with optimized layouts.
 * Platform Infrastructure: Create reusable frameworks, enforce data contracts, build CI/CD pipelines, and establish observability practices.
 * Data Quality: Implement validation frameworks, data contracts, and SLIs/SLOs for critical pipelines.


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