Data Engineer

E-commerce got real-time data infrastructure decades ago. Physical stores still have not. RADAR is changing that. RADAR is building the data infrastructure layer for the physical world, starting with retail. Our hardware‑enabled SaaS platform uses proprietary overhead sensors, software, and AI‑powered analytics to locate every product in a store, continuously, down to the fixture. We are deployed across 1,400+ stores with retailers including American Eagle Outfitters and Old Navy, processing tens of billions of real‑world events every day, delivering 99%+ accuracy in complex, noisy environments at fleet scale.

RADAR is one of the best‑funded companies in retail technology, backed by a recent Series B financing at a $1 billion valuation. Inventory accuracy is only the beginning. We believe RADAR can become foundational infrastructure for the physical economy, powering new AI‑driven commerce experiences across retail and beyond. Join us if you want to work on a large, unsolved, technically challenging problem with an ambitious team building category‑defining technology.

• Mission-Driven:
  We're transforming retail with cutting‑edge technology and building something that truly matters.
• Collaborative Team:
  We thrive on curiosity, shared goals, and solving complex problems together.
• High Impact:
  You’ll make meaningful contributions from day one and help shape the future of our product and company.
• Clear Communication:
  We value honesty, humility, and respectful dialogue—everyone’s voice matters.
• Balanced Lives:
  We work hard, but not at the expense of well‑being. We respect time, boundaries, and life outside of work.
• Diverse Perspectives:
  We believe better ideas come from diverse backgrounds, experiences, and viewpoints.
• Empathy-Driven Design:
  We build with deep respect for our end users, listening closely to their feedback and needs.

We are looking for a Staff Data Engineer to help build and develop our analytics, Machine Learning and AI capabilities  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.

• 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).

• Experience building pipelines that support ML…