Analytics Engineer

United States·OremFull-Timemid

Data EngineerData

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Quick Summary

Overview

We're building a modern analytics platform from the ground up: a layered, well-tested data model in Snowflake that becomes the single source of truth for the business,

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Data EngineerData

We're building a modern analytics platform from the ground up: a layered, well-tested data model in Snowflake that becomes the single source of truth for the business, and a governed semantic layer that lets stakeholders, not just analysts, ask questions and get trustworthy answers.

As an Analytics Engineer, you own the transformation layer. You'll turn messy source data from a dozen operational systems into canonical facts and dimensions, migrate legacy reports onto that clean foundation, and make the whole thing reliable enough that people stake real decisions on it.

Responsibilities

~1 min read

→Strong SQL — you can read, write, and judge it. Window functions, deduplication, incremental logic, and grain are second nature, whether the first draft came from you or from an AI assistant.
→The judgment to work with AI tooling, not be replaced by it: you can tell when generated SQL is subtly wrong, and you take ownership of fixing it.
→Hands-on dbt experience (Core or Cloud): models, tests, macros, refs/sources, and a feel for layered project structure.
→Experience with a cloud warehouse, ideally Snowflake.
→Comfortable with Git/GitHub and a PR-based, review-driven workflow.
→Dimensional modeling fundamentals (facts, dimensions, SCDs) and the judgment to avoid over-engineering.
→A documentation and testing habit: you make your work legible and verifiable.

Experience using AI coding assistants (e.g., Claude Code, Copilot, Cursor) in a professional, review-gated workflow.
ELT tooling (Fivetran, CData) and experience taming third-party source schemas.
Semantic layer/metrics layer work, or experience preparing data for AI/LLM consumers.
BI tooling (e.g., Power BI, Sigma) and partnering directly with report consumers.
Domain exposure to real estate, property management, finance/GL, or operations.
Python for ancillary tooling and automation.

Greenfield with guardrails. You're building the platform, not babysitting legacy — but with real standards, code review, and CI from day one.
AI-accelerated, human-owned. We give you the best modern tooling to move fast, and we trust you to own the outcome. Less time on boilerplate, more time on judgment.
Your work ships decisions. The models you build directly drive how communities are run and capital is deployed.
Craft is valued. Small diffs, clean models, and good tests are how we measure quality here — not heroics.