Analytics Engineering Lead

• Lead the planning and delivery of analytics engineering initiatives across business domains, aligning work with strategic priorities.

• Own the delivery of scalable data models and datasets, coordinating contributions from other engineers where required.

• Partner with stakeholders across Product, Marketing, Finance, Data Science, and Engineering to define requirements and shape solutions.

• Challenge and influence stakeholders to drive scalable, sustainable, and high-impact data solutions.

• Act as a technical leader for analytics engineering, promoting best practices in data modelling, testing, documentation, and governance.

• Design and implement scalable, reusable data models using dbt and Snowflake.

• Lead architectural decisions, balancing performance, cost, scalability, and usability.

• Contribute to the evolution of analytics engineering standards, tooling, and data platform capabilities.

• Make and own technical decisions, balancing trade-offs between speed, scalability, and cost.

• Ensure high standards of data quality through testing, monitoring, and governance practices.

• Use metrics such as data quality, pipeline performance, and adoption to drive continuous improvement.

• Identify opportunities to optimise processes, tooling, and workflows.

• Translate complex business and data challenges into scalable data models and actionable delivery plans.

• Advocate for best practices in data modelling, governance, and data usage across the organisation.

• Represent Analytics Engineering in cross-functional discussions, helping teams navigate priorities and trade-offs.

• Mentor and support analytics engineers through code reviews, pairing, and knowledge sharing.

• Contribute to raising the overall quality, consistency, and maturity of the analytics engineering function.

• Support the optimisation and scalability of the Snowflake data platform, ensuring performance, security, and cost efficiency.

• Evaluate and introduce new tools and technologies that improve platform capability and engineering effectiveness.

• Drive adoption of standardised, well-documented data models across the business.