Senior Data Engineer - Data Lead
Foundry Robotics
ABOUT US Foundry Robotics is building an AI-native robotics manufacturing company focused on deploying advanced assembly and production capability for leading robotics companies and national-security-critical hardware. Basically, we’re building robots that build robots. We are reimagining manufacturing through advanced robotics. Our mission is to rebuild the American manufacturing industry as an AI-first, assembly-focused, dual-use contract manufacturer. We aim to empower manufacturers with intelligent, efficient, and adaptable robotic systems that redefine productivity and quality. THE ROLE We are hiring a Sr Data Engineer / Data Lead to own the data layer that powers Factory OS. You will design and operate petabyte-scale data pipelines that ingest visual, telemetry, and manufacturing data from the factory floor, move it reliably across edge, on-prem, and cloud environments, and make it available for ML training, analytics, and operational decision-making. You will define the data roadmap—architecture, governance, lifecycle, and tooling—and be the person the rest of the engineering org depends on for clean, reliable, well-modeled data. You will also contribute to backend services where data meets application logic. This is a hands-on engineering role. You will ship. KEY RESPONSIBILITIES Petabyte-Scale Data Pipelines - Design, build, and operate PB-scale data pipelines for ingesting, curating, indexing, and preparing manufacturing, visual, and telemetry data - Implement reliable data movement across embedded, edge, on-prem, and cloud compute environments - Build streaming and batch processing systems that handle high-throughput factory-floor data in real time - Implement data lifecycle management: retention, archival, compaction, and cost optimization at scale Data Architecture + Roadmap - Define and own the Factory OS data roadmap—architecture, governance, quality, and tooling strategy - Design data models, schemas, and contracts that serve application, ML, and analytics consumers - Establish data cataloging, lineage tracking, and discoverability across the platform - Drive data quality standards: validation, monitoring, alerting, and anomaly detection - Evaluate and adopt data technologies (warehouses, lakehouses, streaming platforms) as the platform scales Backend Services + Integration - Contribute to backend services where data pipelines meet application logic (APIs, event-driven systems, database layers) - Partner with ML engineers to ensure training and inference pipelines have access to clean, well-prepared datasets - Collaborate with full-stack developers, robotics teams, and operations to translate data needs into reliable infrastructure - Implement observability: metrics, logging, and tracing across data systems WHAT WE'RE LOOKING FOR - Between 5-7 years of experience in data engineering, with demonstrated work at terabyte-to-petabyte scale - Deep hands-on experience with data pipeline frameworks (Spark, Flink, Kafka, Airflow, dbt, or similar) - Strong proficiency in Python and SQL; backend experience in Go, Java, or TypeScript is a plus - Experience designing data architectures spanning streaming, batch, lakehouse, and warehouse patterns - Solid understanding of distributed systems, storage engines, and data modeling (relational and NoSQL) - Experience with cloud data services (AWS S3/Glue/Redshift, GCP BigQuery/Dataflow, or Azure equivalents) - Comfortable defining roadmaps, making architectural decisions, and driving alignment across teams - Comfortable operating independently and owning complex systems end-to-end NICE TO HAVE (NOT REQUIRED) - Experience with visual or image data pipelines at scale (video ingestion, frame extraction, annotation workflows) - Background in manufacturing, robotics, or industrial IoT data systems - Familiarity with ML data preparation workflows (feature stores, dataset versioning, labeling pipelines) - Experience with edge-to-cloud data architectures and intermittent connectivity pat