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