Data Engineer

Responsibilities

• Data ingestion and integration
• Build and operate the pipelines that pull in high-volume process data from Seurat's print systems and adjacent equipment.
• Integrate data from heterogeneous sources, including telemetry streams, in-situ process monitoring (optical, thermal, imaging), build job metadata, powder lot and material records, and post-build inspection such as metrology, microscopy, and mechanical tests.
• Efficiently store and retrieve large binary artifacts such as images, layer-wise scans, and sensor traces, alongside more structured data.
• Data cleaning and normalization
• Add validation, deduplication, and normalization so that downstream consumers do not have to keep rediscovering the same data quality issues.
• Define and maintain schemas across data sources whose formats change as the hardware and process evolve.
• Work with hardware, process, and software engineers to address data quality problems at the source rather than only in flight.
• Data lake and storage
• Design and run Seurat's data lakehouse, including partitioning strategies, file formats such as Parquet, HDF5, and Zarr, schema enforcement, retention, and cost management.
• Build catalog and lineage tooling so that engineers can find and understand the data they need without relying on tribal knowledge.
• Pick the right storage tier for each workload across object storage, time-series, relational, and search systems.
• Data access and tooling
• Provide well-documented access patterns, including query interfaces, APIs, and notebooks, so that process engineers, analysts, and ML practitioners can be productive without becoming infrastructure experts themselves.
• Manage access control, governance, and auditing sensitive or proprietary data.
• Build internal tooling that lowers the cost of common data tasks, including extraction, joining, exploration, and sharing.
• Reliability and operations
• Instrument pipelines for observability and respond to failures and data quality regressions.
• Establish testing patterns appropriate to data systems, including contract tests, data quality checks, lineage verification, and backfills.
• Key Goals and Expected Outcomes
• A reliable, well-organized process data foundation. Process and inspection data is ingested, cleaned, and made accessible without ad-hoc effort from downstream consumers.
• Measurable improvements in data quality. Freshness, completeness, and correctness across the most important datasets are tracked and trending in the right direction.
• Self-service. Process engineers, analysts, and AI/ML engineers can answer their own data questions with confidence in the underlying data.
• A scalable platform. The data foundation grows with Seurat's print fleet, sensor coverage, and product diversity without requiring proportional headcount.

Requirements

• Bachelor's degree in Computer Science, Data Engineering, or a related technical field, or equivalent practical experience.
• 3+ years of professional experience as a Data Engineer or in a closely related role.
• Strong Python and SQL (PostgreSQL).
• Experience with time-series databases such as TimescaleDB, streaming systems such as Kafka or Kinesis, and observation and reporting systems such as Grafana.
• Experience working alongside physics, optics, materials, or hardware engineering teams, and having a clear und

Additional Information

About Seurat Seurat is transforming manufacturing for people and our planet by delivering a scalable additive manufacturing solution to fundamentally change how products are made. Seurat's proprietary Area Printing process, developed at Lawrence Livermore National Labs (LLNL), allows metal components to be manufactured at price points and quality levels that compete directly with conventional manufacturing techniques, enabling the reshoring of supply chains and promoting the decarbonization of industry. Seurat has raised over $180M and is backed by leading venture partners like Capricorn, NVentures (NVIDIA), True Ventures, General Motors Ventures, Denso, Porsche SE, SIP global partners, Honda, Xerox Ventures/Myriad Venture Partners, Cubit Capital, Siemens Energy, and Maniv Mobility. Position Overview Seurat is looking for an experienced Data Engineer to own and expand the upstream data foundation behind our process engineering work. The role covers ingestion, cleaning, organization, governance, and access. You will design and operate the pipelines and storage that turn raw output from numerous materials studies, machine qualification trials, application development runs, and customer builds into reliable, well-organized data that engineers, scientists, and downstream analysis or AI/ML systems can rely on. This is a platform role. The most common day-to-day work is making our process engineers, data analysts, and ML practitioners more productive by giving them dependable data and good ways to get at it. The role is not focused on analysis or model development, although you will work closely with the people who do that work.

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