Design and build robust dbt models on Databricks that transform raw, ingested data into clean, conformed, and analytics-ready datasets.
Define and implement KPI logic in collaboration with business and analytics stakeholders, ensuring consistent definitions across domains.
Maintain and evolve the semantic/presentation layer, ensuring data products are reliable, tested, documented, and performant.
Apply software engineering best practices to analytics code: version control, testing, CI/CD, and documentation.
Independently onboard new data domains (e.g. marketing attribution, product usage, customer care, subscription data) with limited guidance — exploring the data, understanding its structure and meaning, and deciding how to best model it.
Proactively engage business partners and domain owners to understand context, validate assumptions, and align on KPI definitions.
Identify data quality issues early and work with the Data Management team to resolve them at source.
Act as the connective tissue between data engineers and analysts: translating analytical needs into engineering tasks, and surfacing data realities back to the business.
Work with the Analytics team to ensure the presentation layer meets reporting and self-service needs.
Contribute to data governance: naming conventions, lineage documentation, and model cataloguing.
Support the broader team in extending analytics coverage to new brands and domains over time.

5+ years of experience in analytics engineering, data engineering, or a closely related data role.
Strong, hands-on proficiency with dbt (dbt Core or dbt Cloud) — this is a core requirement.
Experience working on Databricks (or a comparable cloud data platform such as Snowflake or BigQuery).
Solid understanding of dimensional modelling, data vault, or similar data warehousing patterns.
SQL excellence: complex transformations, window functions, query optimisation.
Proven ability to work autonomously across multiple data domains simultaneously, figuring out unfamiliar data with limited documentation.
Strong analytical mindset: able to interrogate data critically, spot anomalies, and validate logic end-to-end.
Excellent communication skills — comfortable talking directly with business stakeholders to elicit requirements and explain data concepts.
Experience working across diverse data domains (e.g. marketing, product analytics, customer care, financial/subscription data).

Analytics Engineer

Key skills