Data Engineer
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Summary
A hands-on data engineer at JBREC (a housing research firm) who builds the governed analytics layer in dbt: migrating legacy SQL Server/T-SQL logic into tested, documented models, implementing metrics in a semantic layer, and keeping ingestion pipelines reliable. Remote US role needing 3+ years data/analytics engineering incl. 2+ years production dbt.
Position Title: Data Engineer
This role is not eligible for visa sponsorship. Candidates must be authorized to work in the U.S. now and in the future without sponsorship.
Reporting Structure:
This position will be part of the Data Engineering group, reporting to the Director of Data Engineering.
About the role
John Burns Research and Consulting (JBREC) is a housing research and consulting firm. Builders, investors, and lenders use our data to make decisions about where and what to build. That data has to be right, consistent, and explainable.
We are hiring a Data Engineer to help build our governed analytics layer in dbt. You will turn legacy SQL Server logic into tested, documented dbt models. You will also implement the metrics behind our client data feeds, dashboards, reports, and AI-assisted analytics tools, and recommend how they should be structured.
This is a hands-on individual contributor role. Most of your time will go to data modeling and metric implementation. The rest will go to keeping our newer pipelines reliable.
What you'll do:
Build the governed model layer (about 50%)
- Build and maintain dbt models across staging, intermediate, and mart layers, following team conventions for naming, grain, and documentation.
- Migrate legacy SQL Server and T-SQL logic into dbt. Reconcile new outputs against existing ones before cutover so client-facing numbers don't move unexpectedly.
- Write tests and data contracts that catch problems before clients or analysts do.
- Document every model so its meaning, source, and owner are clear to anyone who reads it.
Implement metrics in the semantic layer (about 25%)
- Partner with research and data science to turn agreed business definitions into precise, testable implementations with explicit grain, dimensions, and calculation logic.
- Recommend how metrics should be structured and implemented, and flag inconsistencies or ambiguities in existing definitions.
- Maintain metrics in our semantic layer as the single source for client data feeds, dashboards, PDF reports, and natural-language analytics tools.
- Help retire legacy reporting by moving its metrics onto the governed layer one at a time.
Keep our newer pipelines reliable (about 25%)
- Build and maintain ingestion pipelines on our modern stack for third-party vendor data and public data such as Census releases.
- Own day-to-day reliability for those pipelines: monitoring, alerting, triage, and runbooks that let anyone on the team resolve issues.
Throughout
- Work in Git with code review and CI. Review teammates' code and expect yours to be reviewed.
- Use AI coding tools effectively within our review and governance standards.
What you bring
- 3+ years in data engineering or analytics engineering, including at least 2 years building production dbt projects.
- Strong SQL and dimensional modeling. You can explain why a model has the grain it has and what breaks if it changes.
- Hands-on experience with dbt testing, documentation, and project structure at scale (dozens to hundreds of models).
- Experience migrating legacy SQL (stored procedures, views, T-SQL) into a modern transformation layer, including validating that outputs match.
- Working Python for ingestion, automation, and tooling.
- Comfort with Git-based workflows: branches, pull requests, code review, CI.
- Clear writing. You document your work so others can use it without asking you.
Nice to have
- Experience with a semantic layer or metric framework (dbt Semantic Layer/MetricFlow, Cube, LookML, or similar).
- Experience supporting natural-language or AI-assisted analytics, where metric definitions must be unambiguous.
- Familiarity with Azure data services and SQL Server.
- Experience with housing, real estate, economic, or Census data (ACS, PUMS, building permits).
How we work
You'll join the Data Engineering team within JBREC's technology organization, which also includes Software Engineering, Data Science, and IT. We treat governance as code: definitions, tests, and documentation live in the repository and go through review, rather than in separate catalog tools. You'll work closely with our research team, who are the experts on what the numbers mean.
Each full-time employee receives:
- $12,500 towards our comprehensive benefits, including medical, dental, vision, prescription drug coverage, and flexible spending accounts including gym memberships and vacation assistance.
- Company contributes 5% of their salary and profit sharing to their 401(k), even if they do not contribute.
- 30 days of Paid Time Off including 3 weeks of personal time off (PTO), 10 paid holidays, and 5 paid days for our Winter Break when the company shuts down to rejuvenate.
Compensation and title will be based on skills, experience, and abilities, which can vary by person.
We cannot hire anyone who requires sponsorship to work in the United States.
Disclaimer: Nothing in this job description restricts the company’s right to assign responsibilities to this job at any time as critical features of this job are subject to change at any time.