Lead Data Engineer
HybridSolihull, England, United Kingdom
Job Summary
Own and drive the technical design and architecture of data pipelines, data models, and platform components across the team. Set and enforce engineering standards for code quality, testing, observability, security, and documentation. Lead technical discovery, design sessions, and code reviews to elevate team output while identifying and resolving technical debt. Mentor Data Engineers from junior through senior levels, facilitating knowledge sharing and calibrating technical interviews. Partner with Data Governance to embed data quality, access control, and privacy by design. Collaborate cross-functionally with Data Product and wider Tech teams to ensure joined-up platform delivery. 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
- Must be able to attend at least 3 days a week in GSIQ, Solihull, UK
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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