Senior Analytics Engineer

Role overview Turn raw, ingested data into dependable datasets and data products that support reporting and analysis. Working between data engineering and analytics, you’ll define shared metrics, build models and explore varied business domains, from marketing and product usage to customer care and revenue. The role calls for independent judgement: understanding unfamiliar data, checking its meaning with stakeholders and delivering useful models with limited guidance.

Responsibilities - Build and maintain dbt models on Databricks to produce clean, conformed, analytics-ready data. - Define KPI logic with business and analytics partners so metrics are consistent across domains. - Develop the semantic and presentation layers, keeping data products tested, documented, performant and reliable. - Apply software engineering practices such as version control, testing, CI/CD and documentation to analytics code. - Explore new data domains, validate assumptions with domain owners and identify quality issues early. - Translate reporting needs into engineering work and coordinate with data management and analytics colleagues.

Requirements - Experience building analytics data models and transforming raw data into trusted datasets. - Strong SQL and practical dbt experience; familiarity with Databricks or a comparable data platform. - Ability to define metrics and model data across multiple business areas. - Comfort exploring unfamiliar datasets independently and making sound modelling decisions. - Clear communication with technical teams and business stakeholders.

Nice to have Experience with SaaS marketing or product analytics tools, Python, BI platforms such as Looker, Tableau or Power BI, or work in a multi-brand organization. Consulting or contractor experience in complex environments is also useful.