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Salary: USD 170,000 - 300,000 / annual
Hadrian is building autonomous factories to reindustrialize American manufacturing, combining AI, advanced software, robotics, and full-stack manufacturing to help aerospace and defense companies build rockets, satellites, aircraft, and mission-critical systems up to 10x faster and at lower cost. The company recently raised $1.37B in Series D funding at a $7.87B valuation and is rapidly expanding manufacturing capabilities across welding, casting, forging, electronics, and additive manufacturing.
As a Data Platform Engineer, you will own critical segments of the data flow from source to trusted dataset, including ingestion, change data capture, streaming, lakehouse storage, orchestration, transformation, contracts, quality, and lineage. You'll build the data backbone for autonomous factories, transforming machine signals, quality events, work orders, and application changes into trusted data used by scheduling, ML, and operations teams.
Key responsibilities include building ingestion, CDC, and streaming capabilities for transactional data, events, telemetry, and files while managing ordering, deletes, retries, replay, idempotence, and backpressure. You'll define versioned data and event contracts with upstream teams, model telemetry and operational data into reliable datasets, own Dagster orchestration and dbt transformation, and collaborate with Manufacturing Operations and Infrastructure to acquire data from machines, PLCs, historians, and industrial protocols.
Required qualifications include production experience building and operating data infrastructure or distributed data systems with on-call ownership. You need strong production Python, advanced SQL, and data-modeling skills including incremental processing and temporal data. Experience with Kafka or event-streaming platforms, CDC pipelines, Snowflake, lakehouse table formats (Iceberg, Delta, Hudi), Dagster/Airflow, dbt, and Kubernetes is essential. Strong judgment regarding contracts, failure modes, and downstream system needs is critical.
Experience that sets you apart includes running Snowflake and Iceberg together, production experience with PeerDB, Debezium, Flink, or Spark Structured Streaming, proficiency with ClickHouse, experience with industrial data collection using OPC-UA or MTConnect, and background in performance-sensitive systems built with Go, Rust, or Scala.
