Analytics Engineer

Havenpark Communities; United States; On-site; USD 120,000 - 180,000; Full time

Job summary

Havenpark Communities is building a modern analytics platform and seeks an Analytics Engineer to own the transformation layer. You will turn multiple source systems into canonical facts and migrate legacy reports onto the new core model while ensuring reliable, governable data for stakeholders.

You will work with AI-assisted tooling to accelerate development, write robust tests, and maintain high standards for data correctness, performance, and documentation.

Qualifications

• Strong SQL for complex queries, window functions, deduplication, and incremental logic.
• Hands-on dbt experience (Core or Cloud) with tests and macros.
• Experience with a cloud data warehouse, preferably Snowflake.
• Comfortable with Git/GitHub and a PR-based workflow.
• Dimensional modeling fundamentals.

Responsibilities

• Build the Core data model with facts and dimensions following a layered architecture.
• Migrate legacy reporting onto the Core model and reconcile outputs.
• Use AI tooling to accelerate model development while ensuring correctness.
• Write dbt tests and establish data contracts; treat CI failures as blockers.
• Integrate data from diverse sources and document quirks and coverage gaps.
• Curate semantic/metrics layer to enable governed self-service and AI readiness.

Skills

SQL

Dimensional modeling

Code review / ownership

Tools

Snowflake

dbt

GitHub

Job description

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.

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

• 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

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.