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Contoro

Data Engineer, Cloud Platform

Austin, TXfulltimemidAdded 1 month ago

About this role

Contoro Robotics, an Austin-based warehouse automation startup, seeks a Data Engineer to design and maintain a company-wide analytics data pipeline that powers decision-making across teams. You'll own metrics instrumentation, data quality, and self-service analytics capabilities spanning robot and cloud systems.

What you'll do

  • Design and evolve event-driven data architectures for telemetry ingestion, schema evolution, and downstream consumption
  • Own the catalog of metrics, events, schemas, and data contracts across the organization
  • Develop automated validation systems and tests to ensure telemetry accuracy and prevent regressions
  • Build performant SQL queries, time-series analytics pipelines, and pre-calculated metrics for fast reporting
  • Improve reliability, scalability, and observability of data pipelines spanning robots and cloud systems
  • Establish best practices for analytics instrumentation, testing, and governance

What they're looking for

  • Python, Docker, and SQL databases in production
  • Advanced SQL including aggregations, time-series analysis, and optimization
  • Data pipeline technologies (Kafka, MQTT, Fluent Bit, PostgreSQL, TimescaleDB)
  • Event schema design and data contract definition
  • Automated data quality testing and validation
  • Analytics data modeling and metrics API development
  • Cross-functional collaboration and technical communication
  • B.S. in Computer Science, Engineering, or equivalent industry experience
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Contoro

Contoro builds autonomous warehouse automation robots that unload boxes from trailers and containers using computer vision, motion planning, and robotic manipulation. The company is hiring robotics engineers, software engineers, data engineers, and ML specialists to develop perception systems, autonomous control software, analytics infrastructure, and motion planning algorithms for production robot fleets.

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Likely interview questions

  • Walk us through a production data pipeline you built from scratch—what were the key design decisions around schema, ingestion, and validation?
  • How have you handled schema evolution in event-driven systems when downstream consumers depend on that data?
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