Analytics Engineer

Salary

$100,000 - $140,000

Location

Orem, UT

Posted

Today

Build the Core data model. Design and implement facts and dimensions in dbt following a disciplined staging → intermediate → core → mart architecture. Model slowly changing dimensions, handle mixed-grain snapshot sources, and make defensible grain and materialization decisions.
Migrate legacy reporting onto Core. Reconstruct existing business-critical views and reports on top of the new model, reconciling outputs line-for-line so stakeholders can trust the cutover.
Work fluently with AI in the loop. Use AI coding tools to accelerate model development, test writing, and the dbt/GitHub workflow, while critically reviewing every output, correcting it, and taking full ownership of correctness, performance, and style.
Own data quality. Write dbt tests (generic, singular, and unit), establish contracts on data-out models, and treat a failing CI check as a release blocker, not a suggestion.
Integrate diverse sources. Work across data from a myriad of sources, each with its own quirks, grains, and coverage gaps you'll need to understand and document.
Build the semantic / AI-ready layer. Curate mart models and metric definitions with the metadata (certification, PII level, known issues) that powers governed self-service and agentic AI, so non-analysts can safely ask their own questions.
Raise the bar on engineering practice. Small, reviewable PRs; a shared style guide; version control as the source of truth; documentation that the next engineer — or AI agent — can actually use.

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.

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