Staff Data Engineer, Radar

• 8+ years in an Analytics Engineering or Data Engineering role, including experience setting technical direction and mentoring other engineers
• 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, 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)
• Solid grasp of large-scale data fundamentals — partitioning strategies, SQL query performance optimization, cost/performance tradeoffs
• Experience developing data models that support scalable, cost-effective analytics and ML pipelines
• Experience writing data quality checks and unit and integration tests to ensure high-quality data and analytics
• Experience creating analytics solutions with visualization tools such as Looker or Tableau
• Proficiency with version control (Git)

Desirable

• We are looking for a Staff Data Engineer to help build and develop our analytics, Machine Learning and AI capabilities at RADAR.
• The role requires extensive collaboration with teams and functions across the company ranging from product and customer success to engineering, data science and research
• Design, build, and maintain scalable, reliable data pipelines (batch and streaming) with Airflow, Beam, and Python — including the data quality checks, testing, monitoring, and error handling that keep them trustworthy
• Design data models that make analytics and ML pipelines scalable, repeatable, and cost-efficient
• Write and optimize complex SQL (CTEs, window functions) and streaming pipelines in Python
• Partner with data science, engineering, and product to turn data needs into solutions
• Build RADAR's data product offering — turning our real time inventory and location data into actionable insights for our retail partners — and the visualizations and dashboards (e.g. Looker) that turn data into decisions
• Own the technical direction of the data platform — architecture, technology, standards, and roadmap
• Mentor data engineers and set the standards the team builds on for scalable and reliable data pipelines
• In your first 30 days, you will:
• Complete onboarding, security, and compliance training, and get to know RADAR's mission, stack, and values
• Meet key stakeholders across the org and start building relationships beyond your immediate team
• Develop a working understanding of RADAR's data architecture — BigQuery tables, batch and streaming pipelines
• Ship a meaningful contribution to an active pipeline, getting hands-on with the codebase and tooling
• Begin forming a point of view on where the data platform can improve
• In your first 60 days, you will:
• Take ownership of a meaningful component of the data platform — a pipeline, data model, or domain — and be the go-to person for it
• Deliver improvements to reliability, cost, or performance on an existing pipeline, applying data quality checks, testing, and monitoring as you go
• Partner directly with data science, engineering, or product on an active need, translating an ambiguous requirement into a shipped solution
• Turn your early observations into a concrete, prioritized list of where the data architecture and technology stack can improve — and socialize it with the team
• In your first 90 days, you will:
• Own and drive a piece of the data platform roadmap, from proposal through delivery, making the architecture and build-vs-buy tradeoffs it requires
• Establish or level up a team standard for data modeling, and get the team aligned behind it
• Mentor other engineers through code reviews, design discussions, and pairing — raising the bar on how the team builds
• Deliver measurable impact on RADAR's data product offering, helping turn real-time inventory and location data into insights partners can act on