Your Role in Action 

• Own complex data workflows end-to-end, from ingestion to curated layers, ensuring reliability and performance at scale. 

• Architect and optimize ETL/ELT pipelines for diverse data sources, leveraging orchestration tools (Airflow/Prefect) and transformation frameworks (dbt). 

• Design robust data models (dimensional schemas, SCD strategies, semantic layers) that enable high-quality analytics and reporting. 

• Drive technical standards for data quality, lineage, observability, and governance across engineering teams. 

• Mentor and guide junior engineers, fostering a culture of clean code, testing, and continuous improvement. 

• Collaborate cross-functionally with analytics, product, and platform teams to align data solutions with business priorities. 

• Lead performance tuning and cost optimization for Snowflake, Databricks, and other cloud data platforms. 

• Champion CI/CD and infrastructure-as-code practices for data workflows, ensuring repeatability and compliance. 

• Influence roadmap decisions for data engineering capabilities and advocate for scalable, future-ready solutions. 

Core Requirements 

• Expert-level SQL and strong Python for data engineering tasks. 

• Proven experience designing and operating production-grade ETL/ELT pipelines with orchestration tools (Airflow/Prefect). 

• Deep knowledge of dbt for transformation workflows and analytics modeling. 

• Hands-on experience with Snowflake and/or Databricks for large-scale data processing. 

• Strong understanding of data modeling principles, incremental strategies, and schema design for analytics. 

• Expertise in data quality frameworks, lineage tracking, and governance in multi-tenant environments. 

• Familiarity with data quality and observability frameworks (e.g., validation checks, automated tests) and proactive incident management. 

• Solid grasp of CI/CD, version control, and automated testing for data pipelines. 

• Ability to lead technical discussions, make architectural decisions, and influence best practices. 

Nice to Have 

• Experience with Spark for distributed transformations. 

• Exposure to streaming ingestion (Kafka/EventHub) and real-time data processing. 

• Knowledge of cost/performance tuning in Snowflake/Databricks. 

• Familiarity with data catalog/lineage tools and privacy/PII handling. 

• Working knowledge of Terraform/Pulumi for infrastructure-as-code in data engineering. 

Relativity is a diverse workplace with different skills and life experiences—and we love and celebrate those differences. We believe that employees are happiest when they're empowered to be their full, authentic selves, regardless how you identify.

Benefit Highlights:

Comprehensive health, dental, and vision plans

Parental leave for primary and secondary caregivers

Flexible work arrangements

Two, week-long company breaks per year

Additional time off

Long-term incentive program

Training investment program

All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, or national origin, disability or protected veteran status, or any other legally protected basis, in accordance with applicable law.