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Data EngineerSeurat-Technologies • Wilmington, North Carolina, United States
Postuler Maintenant
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Data Engineer

Seurat-Technologies
  • US
    Wilmington, North Carolina, United States
  • US
    Wilmington, North Carolina, United States
Postuler Maintenant

À propos

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.
Key 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.
Qualifications
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 understanding of physical properties and sensor values and their meaning.
Experience designing and operating production data pipelines, for example Airflow, Dagster, Prefect, or similar.
Hands‑on experience with cloud data infrastructure, including object storage (S3 or equivalent) and at least one major data warehouse or lakehouse such as Snowflake, Databricks, BigQuery, or Redshift.
A solid grasp of file formats and storage trade‑offs across formats like Parquet, Avro, JSON, and HDF5.
Practical experience with data quality, schema evolution, and pipeline observability.
Experience supporting AI/ML workflows as a data engineer, including feature stores, training data management, dataset versioning, and labeling pipelines.
Comfort working with engineering and scientific stakeholders to translate vague data needs into durable systems.
Strong communication, and a service‑oriented attitude toward downstream consumers of your data.
Nice to Haves
Experience with manufacturing or industrial data, such as high‑frequency sensor telemetry.
Experience handling large scientific or image‑heavy datasets, including CT scans, layer‑wise build imagery, melt pool monitoring, thermography, and metrology.
Familiarity with metal additive manufacturing, particularly laser powder bed fusion or related laser‑based processes. Useful concepts include build files, scan paths, process parameters such as laser power, exposure, hatch spacing, and layer thickness, powder handling, melt pool dynamics, and in‑situ process monitoring.
Familiarity with Seurat's broader technology stack, including high‑power lasers, optics and photonics, or precision motion systems.
Prior work at a hardware or deep‑tech startup, with comfort for the pace and ambiguity that come with bringing new physical products to market.
Competitive salary and meaningful equity.
The opportunity to shape the data foundation of a category‑defining hardware company.
Work on novel technology with a talented, multidisciplinary team.
A collaborative and supportive work environment.
$130,000 - $165,000 USD
Seurat Technologies is an Equal Opportunity Employer that values employees with a broad cross‑cultural perspective. We strive to create an inclusive environment, empower employees and embrace diversity. We encourage everyone to respond. All applicants will receive fair and impartial treatment without regard to race, color, religion, sex, national origin, ancestry, citizenship status, age, legally protected physical or mental disability, protected veteran status, status in the U.S. uniformed services, sexual orientation, gender identity or expression, marital status, genetic information or on any other basis which is protected under applicable federal, state or local law.
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  • Wilmington, North Carolina, United States

Compétences linguistiques

  • English
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