Lead Data Engineer

This role exists to provide technical leadership within the data engineering team, setting the standard for engineering excellence while remaining deeply hands-on. The Lead Data Engineer drives the design and delivery of scalable, resilient data platforms and pipelines that underpin Gymshark's ambition to be a truly data driven business. Sitting at the intersection of craft and collaboration, this role shapes technical direction, raises the capability of those around them, and ensures the team builds the right things in the right way.

WHAT YOU'LL BE DOING:

Technical Leadership:

•    Own and drive the technical design and architecture of data pipelines, data models, and platform components across the team.
•    Set and enforce engineering standards: code quality, testing, observability, security, and documentation, ensuring the whole team operates to them
•    Lead technical discovery, design sessions, and code reviews, providing constructive, actionable feedback that elevates team output.
•    Identify and drive resolution of technical debt, proactively surfacing risks and proposing pragmatic solutions.
•    Evaluate emerging tools and technologies (within the GCP ecosystem and beyond) and make grounded recommendations to the Data Engineering Manager.
•    Work with the Data Engineering manager to find solutions to complex or ambiguous data engineering problems, unblocking the team and stakeholders.
•    Ensure architectural decisions align with Gymshark's data strategy, platform vision, and evolving business needs.
•    Champion a culture of engineering excellence through example, documentation, and knowledge sharing.

Delivery:

•    Own and drive the technical design and architecture of data pipelines, data models, and platform components across the team.
•    Set and enforce engineering standards: code quality, testing, observability, security, and documentation, ensuring the whole team operates to them.
•    Lead technical discovery, design sessions, and code reviews, providing constructive, actionable feedback that elevates team output.
•    Identify and drive resolution of technical debt, proactively surfacing risks and proposing pragmatic solutions.
•    Evaluate emerging tools and technologies (within the GCP ecosystem and beyond) and make grounded recommendations to the Data Engineering Manager.
•    Work with the Data Engineering manager to find solutions to complex or ambiguous data engineering problems, unblocking the team and stakeholders.
•    Ensure architectural decisions align with Gymshark's data strategy, platform vision, and evolving business needs.
•    Champion a culture of engineering excellence through example, documentation, and knowledge sharing.

People & Craft:

•    Mentor and coach Data Engineers (Junior through Senior), supporting their technical growth and career progression.
•    Facilitate knowledge sharing sessions, pairing, and documentation to build collective capability and reduce knowledge silos.
•    Contribute to hiring: lead technical interviews, calibrate assessments, and help onboard new team members effectively.
•    Partner with the Data Engineering Manager on team development planning, identifying skill gaps and proposing learning opportunities.
•    Ensure team members feel supported, challenged, and set up to do their best work.

Governance & Collaboration:

•    Partner with Data Governance to embed data quality, access control, and privacy by design into all engineering work.
•    Collaborate cross functionally with Data Product, and wider Tech teams to ensure joined up platform delivery.
•    Maintain accurate and comprehensive technical documentation: architecture decision records, runbooks, pipeline specs, and data dictionaries.
•    Uphold data governance, security, and compliance standards across all data engineering activities.

WHAT YOU'LL NEED:

Essential Criteria:

•     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.

Preferred Skills & Experience:

•    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 is desirable.
•    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.

CLOSING DATE: 14th August