About the role
EvenUp is on a mission to close the justice gap using technology and AI. We empower personal injury lawyers and victims to get the justice they deserve. Our products enable law firms to secure faster settlements, higher payouts, and better outcomes for victims injured through no fault of their own in vehicle collisions, accidents, natural disasters, and more. We are one of the fastest-growing vertical SaaS companies in history, and we are just getting started. EvenUp is backed by top VCs, including Bessemer Venture Partners, Bain Capital Ventures, SignalFire, and Lightspeed. We are looking to expand our team with talented, driven, and collaborative individuals who seek to have a lasting impact. Learn more at www.evenuplaw.com . We’re looking for a Data Engineer in San Francisco or Toronto to help build and scale the data platform that powers analytics, experimentation, and decision-making across EvenUp. This role is focused on owning end-to-end data systems, from pipeline development and data modeling to enabling reliable, high-quality insights for the business. You’ll partner closely with Product, Engineering, and Analytics teams to translate business needs into scalable data solutions, while driving high standards for data quality, reliability, and performance. This is a high-impact role with strong ownership and the opportunity to shape the foundation of our data platform as we continue to grow. What you’ll do: Build and scale the data platform that powers analytics, experimentation, and business intelligence. Design, implement, and maintain reliable ELT/ETL pipelines and reusable data models. Develop and optimize data infrastructure using tools like dbt, Airflow or Dagster, and warehouses such as BigQuery or Snowflake. Own data pipelines supporting product instrumentation, reporting, and experimentation. Partner cross-functionally with Product, Engineering, Data Science, ML, and Operations to deliver scalable data solutions. Design data models, define source-of-truth metrics, and improve overall data architecture. Enable analytics and reporting by supporting BI tools such as Looker, Tableau, or Metabase. Ensure high data quality through testing, monitoring, and observability best practices. Establish standards for data reliability, governance, and performance. Mentor team members and contribute to a strong, collaborative data engineering culture. What we look for: 4+ years of experience as a Data Engineer or Software Engineer, ideally in product-focused, high-growth environments. Strong proficiency in Python for data wrangling, orchestration logic, and navigating application codebases. Strong proficiency in SQL and experience using transformation tools such as dbt to build reusable data models. Experience designing dimensional models and defining clear, trusted business metrics. Experience building scalable, reliable data pipelines in modern cloud environments. Familiarity with data warehouse technologies such as BigQuery or Snowflake and orchestration tools such as Airflow or Dagster. Experience enabling reporting and visualization solutions using dashboarding tools (e.g., Looker, Tableau, Metabase). Strong communication and collaboration skills when working with cross-functional stakeholders. High attention to detail, structured thinking, and a bias toward action in fast-paced environments. Leadership potential with interest in mentoring others and shaping data engineering best practices.
