Senior / Staff Data Engineer

Revv is on a mission to revolutionize the global auto repair industry and shape the future of safer, smarter roads. In August 2026, Revv merged with AirPro Diagnostics, a premier provider of remote automotive diagnostics and ADAS calibration solutions, bringing together two industry leaders to create a comprehensive, end-to-end ADAS platform. The combination pairs Revv’s AI-powered software and proprietary automotive knowledge graph with AirPro’s diagnostic expertise, hardware, calibration technology, and nationwide service capabilities.

With the combined scale, technology, and industry expertise of Revv and AirPro, we have an opportunity to define the next generation of automotive repair technology. Read more about the merger here!

We've just combined two businesses, two clouds, and more than ten years of operating data. We believe the shared data across Revv and AirPro is one of the most strategically important assets. That data already powers customer-facing products, such as our AI that identifies which ADAS calibrations a vehicle needs, and it is the foundation for how we understand and run the business.

We're looking for a senior or staff-level data engineer who can walk into a complex, multi-cloud environment, make sense of it quickly, and chart a practical path forward. You'll have real ownership and not much hand-holding. We value momentum over perfection and clarity over cleverness.

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You'll report to our Head of Product Analytics, with a dotted line to engineering leadership on the AirPro platform side. You'll partner daily with our analytics team and regularly with finance, sales, and product leadership.

• Typically 6+ years in data engineering, including ownership of platform-level design decisions.

• Deep, hands-on production experience with Snowflake and dbt.

• Strong Python and SQL.

• Working familiarity with the Azure data ecosystem (SQL Server, Data Factory, Synapse, or similar). You don't need to be an Azure specialist, but you shouldn't be starting from zero.

• Sound architectural judgment: you weigh tradeoffs in ingestion, orchestration, and modeling, and can explain clearly why you'd choose one path over another.

• Comfort with ambiguity. You've walked into situations without a playbook, figured out what mattered most, and made progress without waiting for perfect information. When you're blocked, you find a way around it.

• A habit of making your work understandable. You document models, decisions, and strategy so that people outside engineering can use and trust the data.

• Experience partnering across functions and translating business questions into data requirements, and back again.

• Experience at a SaaS or software company.

• Building data that powers customer-facing products, including ML or AI features.

• Integrating data across clouds or after an acquisition or merger.

• Fivetran and modern orchestration tools (Airflow, Dagster, or similar).

• Establishing data governance, SOC 2 controls, or data residency practices.

No automotive or insurance background is required.

Remote anywhere in the US. Most collaboration happens on Eastern Time; adjacent time zones are fine with some flexibility. [Travel: e.g., occasional team onsites.]