Staff Data Engineer

Join RADAR as a Staff Data Engineer and help build and develop our analytics, Machine Learning, and AI capabilities. This role requires extensive collaboration with various teams across the company, including product, customer success, engineering, data science, and research. You will design, build, and maintain scalable data pipelines, optimize complex SQL, and partner with data science, engineering, and product teams. Additionally, you will own the technical direction of the data platform, mentor data engineers, and contribute to RADAR's data product offering.

• Design, build, and maintain scalable, reliable data pipelines (batch and streaming) with Airflow, Beam, and Python.
• Partner with data science, engineering, and product to turn data needs into solutions and build RADAR's data product offering.
• Own the technical direction of the data platform — architecture, technology, standards, and roadmap, and mentor data engineers.
• Experience developing, maintaining, and monitoring large data pipelines with an orchestration tool (Airflow, Dagster, or dbt) for batch and a streaming framework (Apache Beam, Kafka Streams, Flink, or similar)
• Experience writing data quality checks and unit and integration tests to ensure high-quality data and analytics
• 8+ years in an Analytics Engineering or Data Engineering role, including experience setting technical direction and mentoring other engineers
• Experience creating analytics solutions with visualization tools such as Looker or Tableau
• Proficiency with version control (Git)
• Solid grasp of large-scale data fundamentals — partitioning strategies, SQL query performance optimization, cost/performance tradeoffs
• Strong proficiency with large-scale query tools such as SQL or Apache Spark, and comfort with Python for data manipulation and building orchestration and streaming pipelines
• Experience developing data models that support scalable, cost-effective analytics and ML pipelines
• Experience building pipelines that support ML model training and serving
• Experience with modern data warehouses such as Snowflake, Databricks, or BigQuery
• Experience with containerization tools such as Docker
• Bachelor's or Master's degree in a relevant field, or equivalent practical experience