Relativity-posted 1 day ago
$103,000 - $155,000/Yr
Full-time • Mid Level
Hybrid
1,001-5,000 employees

Relativity powers the world’s most critical legal, compliance, and investigative work. From corporate compliance to human rights, our platform must preserve trust in global investigations while handling petabytes of sensitive evidence. Our team builds the distributed data backbone that powers AI-assisted evidence analysis across billions of documents daily. We are at the forefront of Legal Data Intelligence, building technology that helps organizations Organize Data, Discover Truth, and Act on It. As an Advanced Data Engineer, you’ll design and operate data systems that move fast, scale cleanly, and unlock meaningful insights. You’ll work across cloud platforms, modern orchestration tools, and streaming frameworks. You’ll help shape foundational datasets, strengthen our analytical backbone, and contribute to the data capabilities that make AI driven legal intelligence possible. This role is ideal for an engineer with 2 - 4 years of experience who wants deeper ownership, harder problems, and broader impact.

  • Build and operate reliable, well-tested data pipelines using tools like dbt, Spark, Kafka, and cloud-native services.
  • Model, transform, and enrich structured and unstructured data to support analytics, product features, and AI/ML initiatives.
  • Partner with data scientists, AI/ML engineers, and product teams to deliver highquality datasets for experimentation and production use.
  • Contribute to cloud-first data architecture.
  • Participate in design discussions, incident reviews, and code reviews that elevate engineering standards across the team.
  • 2 - 4 years of professional experience as a data engineer or software engineer working with data-intensive systems.
  • Understanding of data modeling, warehouse/lake concepts, and end-to-end pipeline design.
  • Ability to clearly communicate technical decisions and trade-offs.
  • A desire to use data not just to build pipelines, but to enable better decisions and better AI outcomes.
  • Hands-on work with Databricks or cloud-native data stacks.
  • Experience with streaming data processing.
  • Background implementing observability, cost controls, or performance tuning in distributed data systems.
  • Interest in contributing to team standards, patterns, and engineering practices.
© 2024 Teal Labs, Inc
Privacy PolicyTerms of Service