Key Responsibilities- Model the business. Build and maintain clean, tested, documented dbt models in Snowflake, organized in clear staging, intermediate, and mart layers.
- Partner with stakeholders. Work directly with teams across the company to understand what they're trying to accomplish, ask the questions behind the question, and steer requests toward something meaningful rather than just something requested.
- Build the last mile. Create dashboards, explorations, and self-serve datasets in our BI tools so teams can answer their own questions with confidence.
- Own metric and data governance. Define and maintain a shared set of metric definitions, document them, and resolve discrepancies when numbers don't match.
- Keep the data trustworthy. Add tests, monitoring, and alerting so we find data issues before our stakeholders do, and help triage and fix them when they surface.
- Work alongside data engineering. Collaborate on ingestion, warehouse performance, cost, and help define how raw data lands so downstream models stay reliable.
- Ship like an engineer. Use Git, pull requests, code review, and CI to make changes safely and repeatedly.
- Use AI to move faster. Apply AI tools in your daily workflow for writing and reviewing SQL, documentation, exploration, and debugging, and help the team figure out where AI genuinely helps and where it needs a human check.
- Improve the program. Help define the best practices, standards, and documentation the team follows as we build out our analytics practice.
Requirements- 3-5 years in analytics engineering, data engineering, or a similar role.
- Strong SQL: fluent in CTEs and window functions, and able to untangle someone else's query.
- Hands-on dbt and a cloud warehouse, ideally Snowflake, with a solid grasp of modeling patterns, testing, and documentation.
- Python for data work and automation.
- Experience with a modern BI tool (Metabase, Hex, Sigma, Omni, Looker or Tableau) and the speed to pick up the others.
- Stakeholder fluency: you turn a vague ask into a defined problem, explain tradeoffs plainly, and push back when it helps.
- Genuine curiosity about how the business works, because understanding the why is how you build things that get used.
- A working grasp of metric governance, data governance, and observability.
- Git workflows on GitHub or GitLab.
- AI in your daily workflow, with the judgment to verify what it gives you.
- Appetite for a fast-moving company where you'll often build the process yourself.
- Nice to haves:
- Airbyte or another ingestion/orchestration tool (ex: Airflow, Dagster)
- Experience building a semantic layer (dbt Semantic Layer, Omni, LookML, or similar), especially one that serves both humans and AI agents.
- Comfort with command line
Skills and Knowledge- Fluency with SQL and working knowledge of dbt + lakehouse workflows
- Working knowledge of BI/Viz tools
- Working knowledge of ELT/ETL tools (e.g., Fivetran, Airbyte) for batch use cases.
- Working knowledge of data observability/reliability practices - automated alerting, lineage tracking.
- Fluency with AI-augmented development tools (e.g., Claude, Copilot) to accelerate engineering and documentation.
Compensation:Harbor Compliance's base salary range for this role is listed below. Compensation at the time of offer is based on factors such as skill set, experience, qualifications, and work location. Salary is one part of Harbor Compliance's total compensation package. Other benefits may include health benefits, flexible paid time off, parental leave, fertility and adoption assistance, 401(k), and educational reimbursement. Note that the salary range and benefits apply only to U.S.-based candidates.
The pay range for this role is:
93,800 - 117,300 USD per year (US National)
