Senior / Staff Data Engineer

Revv is hiring a senior or staff-level data engineer to build and guide a data platform spanning Revv and AirPro’s multi-cloud environment. The role includes integrating company data, developing trusted data models, connecting business systems, and establishing data governance and quality foundations.

Responsibilities:

• Assess and set the strategy. In your first months, map data needs across engineering, product, finance, sales, and operations, evaluate the current state of our infrastructure, and lay out a plan for a data platform that scales across AWS and Azure
• Integrate two companies' data. Bring AirPro's production data (SQL Server on Azure) into our Snowflake data lake alongside Revv's existing AWS → Fivetran → dbt → Snowflake pipelines. We're considering Azure Synapse for this; you'll pressure-test that choice and own the architecture
• Model data the business can trust. Build clean, tested, well-documented dbt models that our analytics team builds on in Power BI and Hex
• Connect our business systems. Partner with finance and sales to integrate Salesforce and our ERP so revenue and operating metrics are consistent across the company
• Define the foundations. Help establish how we handle data governance, quality, and ownership, including our SOC 2 and international data handling requirements
• Turn data into an asset. As the integration matures, help shape how connected data across every customer touchpoint becomes new products and better experiences for the shops we serve

Requirements:

• 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