Data Engineer, Relativity Space

• If you're interested in building world-class data solutions for diverse stakeholders, bridging the gap between raw information and actionable insights, and in learning how data flows from initial collection all the way through business decision-making, this is the team for you
• 2+ years of experience in data engineering or related field
• Experience with cloud data warehouses, particularly Amazon Redshift
• Experience with dbt for data transformation and modeling
• Knowledge of workflow orchestration tools like Argo or alternatives (Airflow, Prefect, Dagster)
• Strong programming skills in Python and SQL
• Experience with version control systems (Git)
• Understanding of data modeling concepts (dimensional modeling, star schema)
• Bachelor's degree in Computer Science, Engineering, or related field, or equivalent practical experience

Desirable

• The Data Warehouse team builds and maintains critical data infrastructure that powers the entire organization. This includes our end-to-end data pipelines, robust storage systems, applications for data modeling and visualization, enterprise analytics platforms, and other tools that our analysts and business users work with on a daily basis. This team works with almost every department across the company, including product engineering teams, finance and operations
• As a Data Engineer II, you will be responsible for developing and maintaining the technical components of our analytics data platform. This includes building scalable data pipelines, optimizing database storage and query retrieval, and collaborating with cross-functional teams to deliver data solutions that meet business requirements. You will work with modern data stack technologies and contribute to the overall data strategy of the organization
• Discover new data sources for ingestion via database connections, APIs, and message queues
• Design, build, and maintain scalable and reliable data pipelines using Airbyte
• Develop and optimize base data models in Redshift using dbt
• Write efficient SQL queries to support analysis
• Observe the data monitoring systems and quality checks, and respond to issues
• Collaborate with analysts and other stakeholders to understand data requirements
• Document data models systems and administrative processes