Analytics Engineer

What you'll do

• 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.

What we're looking for

Must-haves

• 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

• 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.