Lead Data Engineer
On-siteSolihull, England, United Kingdom
Job Summary
Lead the design and delivery of scalable, resilient data pipelines, models, and platform components across the team. Enforce engineering standards for code quality, testing, observability, and security while driving technical discovery, design sessions, and code reviews. Mentor Data Engineers from junior to senior levels, facilitate knowledge sharing, and lead technical interviews to build collective capability. Partner with Data Governance to embed data quality, access control, and privacy by design, and collaborate cross-functionally with Data Product and wider Tech teams. Ensure architectural decisions align with Gymshark's data strategy and evolving business needs.
Required Qualifications
- Strong experience in data engineering in a senior or lead-level technical role
- Deep expertise in Google Cloud Platform: BigQuery, Dataflow, Pub/Sub, Cloud Storage, and Cloud Composer (Airflow) as the primary data platform stack
- Advanced Python and SQL skills, including writing performant, production grade code and conducting rigorous code reviews
- Proven experience designing and building complex, scalable data pipelines using both batch and streaming/event driven patterns
- Strong data modelling skills: dimensional modelling, data vault, or equivalent, with a track record of building well structured, reusable BigQuery data models
- Experience with Dataform (or equivalent SQL based transformation tools) for orchestrating transformations within BigQuery at scale
- Solid understanding of software engineering principles: CI/CD, version control (Git), testing frameworks, and infrastructure as code (Terraform)
- Demonstrated ability to embed data quality, observability, and alerting into pipelines (automated validation, anomaly detection, monitoring)
- Experience leading technical design sessions, owning architecture decisions, and communicating trade offs clearly to both technical and non technical audiences
- Track record of mentoring or coaching engineers and growing technical capability within a team
- Strong cross functional collaboration and stakeholder management skills, with experience translating business requirements into technical solutions
Desired Qualifications
- Experience with DataProc (Spark) for large scale distributed data processing workloads
- Familiarity with Looker or similar BI tooling, and an understanding of how data models feed downstream analytics and reporting
- Exposure to analytics engineering practices and tooling (e.g. dbt conceptual patterns, data contracts, semantic layers)
- Experience in e-commerce or retail data environments
- Familiarity with data mesh or data platform architecture patterns and their practical application in a scaled organisation
- Experience with GCP cost management and BigQuery cost optimisation strategies
- Broader exposure to ML infrastructure, feature engineering pipelines, or data science platform enablement
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