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# Data Engineer

- **Company:** Tripledot Studios
- **Location:** UK (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, Data Validation, Data Hub, SQL Databases, Data Streaming, Data Ingestion, Apache Spark, Change Data Capture, Kubernetes, Machine Learning Operations
- **Published:** October 9, 2026
- **Apply:** https://startup.jobs/senior-data-engineer-tripledot-studios-company-10342434

## About the Role

* Experience building and operating data pipelines, including ingestion, preparation, data quality checks, backfills and orchestration.
 * Working knowledge of Kubernetes, Airflow, SQL and Spark.
 * A practical approach to delivering work, with reliability and consistency in how you maintain systems and work with others.
 * The ability to collaborate constructively with engineers within your team and across studios., * Experience in the games industry. It's a plus, not a requirement; we can help you learn the domain if you bring the right technical knowledge.
 * Experience with change data capture or streaming.
 * Exposure to machine learning workflows.
 * Knowledge of Kubeflow.

## Description

You'll work primarily within the central AI team, coordinating with studio data engineers through their points of contact so that the solutions you build align with their teams' patterns. Over time, you'll rotate into other areas of the team's work, including productionizing and maintaining machine learning pipelines., * Help build the game event data hub and ingest data from multiple studios.

 * Develop and orchestrate data ingestion, preparation, quality checks and backfills.
 * Centralize dataset metadata and data quality information using tools such as OpenMetadata, giving the team visibility into the health of its data assets.
 * Improve monitoring and notifications so teams know when pipelines or data assets need attention.
 * Coordinate changes with studio data engineers to keep shared solutions aligned with their needs and patterns.
 * Contribute to productionizing machine learning pipelines, monitoring them and making fixes as you rotate across the team's responsibilities.