Staff Data Engineer

Relativity is a company that powers critical legal, compliance, and investigative work. As a Staff Data Engineer, you will design, build, and optimize high-performance data systems, ensuring platform resilience and scalability while directly influencing sensitive legal processes.

Responsibilities:

• Architect, build, and operate distributed data systems that process massive volumes of structured and unstructured data
• Design and deliver scalable, secure, and observable data integration and ETL pipelines using Databricks, dbt, and modern workflow orchestration tools—ensuring end-to-end data quality and availability
• Partner with AI/ML engineers and data scientists to develop robust data foundations that accelerate model training, experimentation, and production deployment
• Drive innovation in data infrastructure, workflow automation, and platform resiliency to expand analytical and AI capabilities
• Collaborate closely with product, AI/ML, and platform teams to deliver data solutions that support critical business and customer outcomes
• Provide architectural direction and design guidance across projects, ensuring alignment with platform strategy and engineering best practices
• Mentor engineers, contribute to engineering standards, and champion a culture of high performance and reliability

Requirements:

• 8+ years of experience in data engineering, data architecture, or backend systems development at scale
• Proven ability to design and optimize complex, high-volume data systems and pipelines for performance, scalability, security, and reliability
• Deep expertise in data modeling, ETL/ELT methodologies, and workflow orchestration
• A strong record of technical leadership—guiding architecture, leading major data initiatives, and mentoring engineers
• Excellent communication and collaboration skills, with the ability to articulate complex concepts to technical and non-technical audiences
• A passion for using data to drive insight and clarity—not just to move pipelines
• Hands-on experience with modern big-data and streaming technologies (e.g., Spark/Databricks, Kafka) in AWS, Azure, or GCP
• Expertise in orchestration and transformation tools (e.g., Airflow, dbt) and cloud data warehouses such as Snowflake or BigQuery
• Background in building data systems that power machine learning or AI-driven applications
• Experience leading modernization or migration of large-scale data platforms
• Familiarity with observability, operational excellence, and cost-optimization strategies for distributed data systems