Lead Data Engineer - Atticus

About Atticus

At any given time, 16 million Americans are experiencing a crisis that requires urgent help from our legal system or government. The right assistance could transform their lives. But today, most never get it.

Atticus makes it easy for any sick or injured American to get life-changing aid. Our mission is to tear down barriers between people in crisis and the aid they deserve. In the last six years, we’ve become the leading platform connecting people with disabilities to government benefits.

We also help victims of accidents, misconduct, and violence get compensation from insurance. So far, we’ve helped hundreds of thousands of people access over $10 billion in life-changing aid —and earned over 20,000 five-star reviews. And we’re just getting started.

We've raised more than $100 million from top VC firms like Fika, Forerunner, Google Ventures, and True Ventures, and we’re on our way to creating a category defining business assisting needy Americans.

The Job

We are looking for our first in-house Data Engineer to own and evolve our core data infrastructure. This is an early and high-impact role. As the data engineering function grows under Engineering, you'll have a real voice in shaping how it's built - the processes, standards, and team culture.

You'll sit at the intersection of our Engineering and Business Operations teams, which means you'll spend your time both building reliable, scalable systems and translating business needs into well-designed data products. You'll work closely with data scientists, business analysts, and product leaders to make sure our data is clean, accessible, and trustworthy.

What You'll Do

• Own and operate our data warehouse, pipelines, and transformation layer
• Design, build, and maintain scalable, reliable data pipelines that ingest data from across our platform and third-party sources, ensuring data is always available and trustworthy for downstream consumers
• Partner with data scientists and analysts to deliver clean, well-documented datasets and optimize query performance so teams spend less time wrangling data and more time generating insights
• Incrementally improve and modernize our existing data systems - you won't build everything from scratch, but you'll know how to assess what we have, prioritize what matters, and migrate thoughtfully
• Implement data quality monitoring, alerting, and documentation practices that build trust across the organization

Qualifications

Required:

• 4+ years of professional experience in data engineering, ideally at a high-growth startup or fast-moving team within a larger organization
• Hands-on experience with the modern data stack - proficiency with BigQuery (or a comparable cloud warehouse), dbt, and an orchestration tool like Dagster or Airflow
• Strong SQL skills and fluency in Golang, Python, or another common Data Engineering language
• Track record of improving or modernizing data systems iteratively - you're comfortable inheriting legacy infrastructure and systems and making them progressively better
• Strong communication and collaboration skills - able to work fluidly across both technical and business-oriented teams

Bonus / Nice-to-Have:

• Experience transitioning data infrastructure from an outsourced or contractor model to an in-house team
• Familiarity with data observability tools
• Experience supporting or collaborating with a data science function, including ML feature pipelines

Location

This job is fully remote with occasional travel to LA (on the company dime) as needed to be with colleagues – usually quarterly, plus offsites.