Analytics Engineer

Role overview Shape the trusted data layer that connects raw data ingestion with business reporting and analysis. You’ll transform source data into consistent models, metrics and semantic datasets, working across domains such as marketing, product usage, customer care and subscriptions. This role suits someone who can investigate a new data area, clarify its meaning with business partners and deliver reliable results with limited direction.

Responsibilities - Create dbt models on Databricks that turn ingested data into clean, conformed datasets for analytics. - Develop shared KPI definitions with business and analytics stakeholders. - Maintain the semantic and presentation layers, with attention to testing, documentation, performance and reliability. - Use version control, automated testing and CI/CD to keep analytics code maintainable. - Onboard unfamiliar data domains by exploring their structure, validating assumptions and choosing appropriate models. - Surface data quality problems and collaborate with data engineers and analysts to address reporting needs.

Requirements - Experience in analytics engineering, data transformation or dimensional modelling. - Strong SQL and dbt skills, with experience using Databricks or a similar platform. - Ability to work across different business data domains and develop consistent metrics. - Independence in investigating data and making well-reasoned modelling decisions. - Confidence communicating with business stakeholders and translating analytical needs into technical work.

Nice to have Familiarity with SaaS go-to-market metrics or tools such as product and marketing analytics platforms; Python for transformation or orchestration; and BI tools such as Looker, Tableau or Power BI. Experience in a multi-brand environment or delivering quickly in consulting or contract work is also useful.