Havenpark Communities

Owns and operates manufactured home communities

Analytics Engineer

Data EngineeringConfirmed live in the last 24 hours

Full-timeOrem, UT, USA$100k - $140k /yrHybrid

About the Job

Requirements

Strong SQL skills, including window functions, deduplication, incremental logic, and data grain.
Ability to critically review AI-generated SQL and take ownership of correcting its correctness, performance, and style.
Hands-on dbt experience with models, tests, macros, references, sources, and layered project structures.
Experience with a cloud data warehouse, ideally Snowflake.
Comfort with Git and GitHub and a pull-request-based, review-driven workflow.
Understanding of dimensional modeling fundamentals, including facts, dimensions, and slowly changing dimensions.
A consistent documentation and testing practice that makes work legible and verifiable.

Responsibilities

Design and implement facts and dimensions in dbt using a staging, intermediate, core, and mart architecture.
Model slowly changing dimensions, handle mixed-grain snapshot sources, and make defensible grain and materialization decisions.
Reconstruct existing business-critical views and reports on the new Core model, reconciling outputs line-for-line for the reporting cutover.
Use AI coding tools to accelerate model development, test writing, and the dbt/GitHub workflow while reviewing and correcting every output.
Write generic, singular, and unit dbt tests; establish contracts on data-out models; and treat failing continuous integration checks as release blockers.
Work across diverse operational data sources, understanding and documenting their quirks, grains, and coverage gaps.
Curate mart models and metric definitions with metadata such as certification, personally identifiable information level, and known issues for governed self-service and agentic AI.
Use small, reviewable pull requests, a shared style guide, version control as the source of truth, and documentation usable by engineers and AI agents.

Desired Qualifications

Experience using AI coding assistants such as Claude Code, Copilot, or Cursor in a professional, review-gated workflow.
Experience with ELT tools such as Fivetran or CData and with managing third-party source schemas.
Experience with semantic or metrics layers, or preparing data for AI and large language model consumers.
Experience with business intelligence tools such as Power BI or Sigma and partnering directly with report consumers.
Domain exposure to real estate, property management, finance or general ledger, or operations.
Python experience for ancillary tooling and automation.

Salary Range: 100000 to 140000 (Currency: USD) (Pay period: per-year-salary)

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.

About the Company

Havenpark Communities

Owns and operates manufactured home communities

Havenpark Communities acquires, improves and operates manufactured home communities across the United States for families, retirees and first-time buyers seeking affordable housing. It brings factory-built houses into residential communities and maintains the shared surroundings, infrastructure and amenities residents use. Residents choose between buying and leasing homes, with local teams handling applications, occupancy and the home-sale process. The business combines long-term property ownership with home sales, leasing and ongoing community management, including maintenance and resident services. Founded in 2015, Havenpark is headquartered in Orem, Utah.

Company size: 201-500
Company stage: Not listed
Total funding: Not listed
Headquarters: Orem, UT
Founded: 2015

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