Build the transformation layer of a greenfield analytics platform and turn operational data into trusted facts and dimensions in Snowflake.

Design and implement facts and dimensions in dbt using a disciplined staging → intermediate → core → mart architecture.
Model slowly changing dimensions, handle mixed-grain snapshot sources, and make defensible decisions on grain and materialization.
Reconstruct existing business-critical views and reports on top of the new model, reconciling results line-for-line for stakeholder trust during cutover.
Use AI coding tools to accelerate dbt model development, test writing, and the dbt/GitHub workflow, while performing critical review, corrections, and full ownership of correctness, performance, and style.
Write dbt tests (generic, singular, and unit) and establish contracts on data-out models.
Treat failing CI checks as a release blocker.
Work across a wide set of sources, documenting and understanding source-specific quirks, grains, and coverage gaps.
Curate mart models and metric definitions with metadata including certification, PII level, and known issues to support governed self-service and agentic AI use cases.
Raise engineering practice through small, reviewable PRs, a shared style guide, version control as the source of truth, and documentation that another engineer or AI agent can use.

Strong SQL: able to read, write, and judge SQL, including window functions, deduplication, incremental logic, and fluency with grain.
Judgment to work with AI tooling: recognize when generated SQL is subtly wrong and take ownership of fixes.
Hands-on dbt experience (Core or Cloud): models, tests, macros, refs/sources, and familiarity with layered project structure.
Experience with a cloud data warehouse, ideally Snowflake.
Comfortable with Git/GitHub and a PR-based, review-driven workflow.
Solid dimensional modeling fundamentals (facts, dimensions, SCDs) and judgment to avoid over-engineering.
Documentation and testing discipline to keep work legible and verifiable.

Experience using AI coding assistants (e.g., Claude Code, Copilot, Cursor) in a professional, review-gated workflow.
ELT tooling experience (e.g., Fivetran, CData) and taming third-party source schemas.
Semantic layer/metrics layer experience, or experience preparing data for AI/LLM consumers.
BI tooling experience (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.

AI is used heavily across the development workflow, including authoring/refactoring SQL, building dbt models, writing tests, and moving work through the GitHub PR process.
AI is treated as a force multiplier, not a crutch or a black box.
You read every line, understand why it is correct, catch what the model got wrong, and stand behind the result in review.

AI tools may support parts of hiring such as reviewing applications, analyzing resumes, or assessing responses to identify inconsistencies or verification signals based on available information.
These tools assist the recruitment team but do not replace human judgment.
Final hiring decisions are made by humans.

Analytics Engineer

Job Description