Analytics Engineer
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Before you apply
LocationOrem, UT
Work modehybrid
Typefull-time
SenioritySenior
DepartmentBusiness Intelligence, Business Intelligence
Company size201–500 people
PostedSep 24, 2026 · 1mo ago
Verified live3d ago
At a glanceSummarised by Seekless from the posting.
Strong SQL — window functions, deduplication, incremental logic, and grain
Judgment to work with AI tooling, not be replaced by it
Hands-on dbt experience (Core or Cloud): models, tests, macros, refs/sources, 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)
Documentation and testing habit
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
Skills
SQL
dbt
Snowflake
Git
GitHub
Claude Code
Copilot
Cursor
Fivetran
CData
Power BI
Sigma
Python
About the role
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.
Hiring process
How we work matters as much as what we build. We leverage AI heavily across our development workflow, authoring and refactoring SQL, building dbt models, writing tests, and moving work through our GitHub PR process. AI is a force multiplier here, not a crutch and not a black box. You own everything that ships under your name. That means you read every line, understand why it’s correct, catch what the model got wrong, and stand behind the result in review. We don’t write everything by hand anymore, but you can’t own what you don’t understand, so strong fundamentals are non-negotiable.
What you’ll do
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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.
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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.
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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.
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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.
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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.
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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.
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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.
What we’re looking for
Must-haves
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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.
Nice-to-haves
•
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.
Why you’ll like it here
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Greenfield with guardrails. You’re building the platform, not babysitting legacy — but with real standards, code review, and CI from day one.
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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.
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Your work ships decisions. The models you build directly drive how communities are run and capital is deployed.
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Craft is valued. Small diffs, clean models, and good tests are how we measure quality here — not heroics.
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