Data Platform Engineer; Manufacturing

12500 Baltimore Avenue, Beltsville, MD 20705, USA

We're hiring a full-time Data Platform Engineer, based in Beltsville, MD and reporting into the Office of the CTO, to take end‑to‑end ownership of the data platforms behind our solid‑state battery manufacturing. You will own the systems that connect our production lines, labs, equipment, and internal applications. You will design, build, and operate the pipelines, databases, monitoring, and back‑end services that move data off the production floor and out of the lab into the dashboards, models, and tools our teams rely on every day.

You'll work hands‑on with real physical‑world data (furnaces, cyclers, robots) on a small team with a modern data stack, across both greenfield and brownfield systems, with visible impact.

This is a role for someone who wants to own data in a manufacturing setting, not a generic big‑data or data‑science position. The right person is fluent on the IT/OT boundary, comfortable writing production code, maintaining existing systems, and partnering across data, IT/OT, and manufacturing teams. We'll set you up with modern AI‑assisted development workflows to amplify your work. The role is open to mid‑level through senior candidates;

we will level to fit. You'll report to and work alongside our Data Solutions Architect, who will support you as you grow into the role.

• Own the data platforms for our manufacturing environment (SCADA, databases, ETL/ELT orchestration, dashboards, and data governance) and administer or support manufacturing business systems (e.g. MES, PLM, QMS) by managing access, permissions, and configuration.
• Build and operate back‑end services, internal tools, and the platform layer (VMs, APIs, Docker/K8s, git, CI/CD), drive incidents to resolution, and lead implementation of new manufacturing tools, coordinating upgrades, testing, validation, and deployment with vendors and stakeholders.
• Minimize single points of failure across the data stack through comprehensive documentation, backup/recovery procedures, cross‑training, and engineering for maintainability.
• Partner with Executive, IT/OT, R&D, Engineering, and Operations stakeholders to scope work, plan deliveries, and roll out changes safely.
• Employ governed AI workflows across day‑to‑day engineering, including code generation, debugging, documentation, code review, and operational automation.
• Serve as a point of contact for manufacturing systems support, develop training materials, and conduct user training.

• Bachelor's in Computer Science, Engineering, Information Systems, or a related field (or equivalent hands‑on experience) and 4+ years building and operating production data systems, or a Master's and 2+ years; spanning database schema design, ETL/ELT workflows and orchestration, and dashboards.
• Experience working with manufacturing, industrial, or adjacent equipment‑generated data (production, lab, sensor, telemetry, or scientific‑instrument), or strong production data engineering experience plus genuine curiosity about physical systems.
• Hands‑on experience with containerization, version control, and CI/CD workflows in a team setting.
• Experience with operational monitoring, logging, alerting, backup recovery, and incident response for production systems.
• Track record of maintaining and improving existing systems, not just building greenfield.
• Experience supporting cross‑functional teams across Engineering, Operations, Quality, and Supply Chain.
• Experience creating reports, dashboards, and KPIs, and gathering requirements to drive process improvements through software systems.
• Experience operating in hybrid environments that span on‑prem infrastructure and cloud services (Azure preferred) and thoughtful about where each system belongs.
• Semantic models, dataflows, and downstream reporting infrastructure (Power BI preferred).
• Internal application development experience – data entry UIs, sample/run trackers, equipment dashboards, or workflow apps tied to production systems.
• Experience working in early‑stage environments where you owned systems end‑to‑end.
• Experience with regulated, quality‑sensitive, or audit‑driven data workflows…