Relativity-posted 2 days ago
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 a Lead Data Engineer, you are the technical leader for your team: hands-on, design focused, and accountable for elevating engineering quality. You’ll drive architectural decisions, guide how systems are built, and mentor others to deliver high-performance, cloud-native data systems. You will work across modern data tooling, including Databricks, Kafka, dbt, and Snowflake, to directly support some of the most mission critical legal processes worldwide.

  • Architect, build, and operate distributed data pipelines and services that process massive volumes of structured and unstructured data.
  • Design and deliver scalable, secure, and observable data integration and ETL solutions using dbt to ensure end-to-end data integrity and availability.
  • Partner with AI/ML engineers and data scientists to build data foundations that accelerate model training, experimentation, and production deployment.
  • Drive improvements in data quality, lineage, reliability, and SLAs, shaping engineering standards for your team.
  • Contribute reusable patterns, frameworks, and best practices that strengthen our Azure cloud-native data platform.
  • 6+ years of experience in data engineering, backend engineering, or data architecture with substantial work on distributed systems and cloud-first data platforms.
  • Deep understanding of data modeling, ETL/ELT design, and workflow orchestration.
  • Proven ability to lead technical decisions within a team—breaking down complex problems, defining trade-offs, and driving alignment.
  • Strong communication skills and the ability to explain complex data concepts to diverse audiences.
  • A commitment to building systems that create clarity and insight through data, not just moving data from point A to point B.
  • Expertise with Databricks and/or Azure-based data architectures.
  • Background building systems that support ML workflows or AI-driven applications.
  • Experience leading modernization or migration of large-scale data platforms.
  • Strong understanding of observability and cost-optimization strategies for cloudbased data systems.
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