Analytics Engineer

Harbor Compliance is launching its first company-wide data and analytics platform, pulling fragmented data from Hub Spot, financial systems, and HRIS into one AI-augmented source of truth for a business that's scaling fast. Modern tools sit underneath and a shared metrics layer makes every number mean the same thing no matter who asks or which tool they ask in. As an Analytics Engineer, you'll join our Data Platform & Analytics team turning raw data into models, metrics, and dashboards the business leans on every day.

You'll talk directly with stakeholders, pick up ambiguous problems and run with them, and have a real voice in how the team works.

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 Git Hub or Git Lab.
• 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…