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Design, build, and maintain dbt/SQL transformations and dimensional models that power self-serve analytics and AI - documented, tested, and built to last. You'll own data quality, lineage, and CI/CD from the ground up, so when someone looks at a number, they trust it. Around 20% of your time you'll work directly with stakeholders, turning business questions into metrics and feeding real-world insights back into the models.
Job Responsibility
Build and maintain dbt models, tests, and documentation for dimensional warehouse schemas (star/snowflake)
Develop and evolve a semantic layer/metrics definitions to enable consistent self-serve in BI and AI tools
Implement data quality checks, lineage, and documentation
monitor data freshness and model performance
Contribute to orchestration and CI/CD workflows (e.g., dbt Cloud/CI, scheduler) with git-based workflows
Partner with stakeholders for light analysis and dashboarding (~20%), translating questions into metrics and iterating on insights
Contribute to standards, conventions, and documentation to scale data development across teams
Requirements
Strong SQL (CTEs, window functions) and hands-on dbt experience across models, tests, and docs
Solid data modelling fundamentals — dimensional schemas, SCDs, and metrics/semantic layer literacy
Git-based workflows, code review habits, and CI/CD basics for data changes
A quality-first mindset: testing, lineage, ownership, and documentation that consumers can rely on
Comfort with at least one BI tool (Tableau, Power BI, Looker) and an eye for good dashboards
The ability to translate business questions into metrics and explain trade-offs without the jargon