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# Data Platform Engineer

- **Company:** Bedrock Robotics
- **Location:** New York, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Airflow, Continuous Integration, Data as a Services, Data Governance, Data Infrastructure, Distributed Systems, Python (Programming Language), Simple Data Format, Software Engineering, SQL Databases, Data Streaming, Workflow Management Systems, Apache Spark, Data Lakes, Kubernetes, Apache Flink, Production Code, Apache Kafka, Build Tools, Databricks
- **Published:** November 3, 2026
- **Apply:** https://startup.jobs/data-platform-engineer-bedrock-robotics-inc-8277130

## About the Role

* 5+ years building data infrastructure or distributed systems in production
 * Deep experience with large-scale storage and compute: data lakes or lakehouses, Spark or similar distributed engines, partitioning and file formats
 * A strong software engineering background: you write production code (Python, Go, Rust, Scala, or similar), not just SQL and configs
 * Experience running infrastructure in the cloud with modern tooling (Kubernetes, infra-as-code, CI/CD)
 * A track record of owning reliability: monitoring, alerting, on-call, and SLOs for systems other teams depend on
 * Good judgment about build-vs-buy tradeoffs and platform design that has to hold up for years

Nice to have

 * Hands-on experience with Databricks
 * Streaming or near-real-time ingestion (Kafka, Kinesis, Flink, or similar)
 * Experience with robotics, autonomous vehicles, IoT, or other edge-to-cloud data systems
 * Large-scale multimodal or sensor data (camera, lidar, telemetry)
 * Orchestration tools like Airflow or Prefect

## Description

You'll own the data platform the rest of Bedrock builds on: the systems that move, store, and serve data from compute on our machines in the field to high-throughput, low-latency services in the cloud. This is an infrastructure role, not a pipeline-maintenance one. You'll design the foundations that autonomy, ML, simulation, and operations teams use to work with data at fleet scale. As the fleet and the number of users grow, you'll make sure the platform scales, stays reliable, and stays cost-efficient.

What you'll do

 * Design and own the architecture of our data platform, from edge ingestion through lakehouse storage, compute, and serving
 * Build systems that get high-volume sensor and log data off machines in the field reliably, including over limited or intermittent connectivity
 * Build and run low-latency, high-throughput data services that internal teams depend on
 * Set platform standards for reliability, SLOs, observability, and incident response
 * Keep performance and cost in check as data volume and compute usage grow
 * Build the self-serve tooling and abstractions that let other engineers ship data workflows without going through you
 * Own data governance, access control, and lineage at the platform level