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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Data Platform Engineer - **Company:** Ion Storage Systems - **Location:** Beltsville, MD, United States - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Application Programming Interfaces (APIs), Airflow, Microsoft Azure, Backup Devices, Code Generation, Code Review, Information Systems, Continuous Integration, Data as a Services, Data Validation, Information Engineering, Data Governance, Data Infrastructure, Extract Transform Load (ETL), Data Systems, Database Schema, Software Debugging, Programming Tools, Disaster Recovery, Supervisory Control and Data Acquisition (SCADA), PostgreSQL, Microsoft SQL Server, Operational Databases, Power BI, Cloud Services, DataOps, Robotic Automation Software, Runbook, Software Engineering, Web Applications, Data Logging, Large Language Models, Backend, Git, Containerization, Kubernetes, Information Technology, Operational Systems, Hardware Infrastructure, Code Restructuring, Software Version Control, Data Pipelines, Automation Anywhere, Docker - **Published:** May 23, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=4850b4115d1ec83d ## About the Role Do you have experience in Version control systems?, Bachelor's degree in Computer Science, Engineering, Information Systems, or related field, or equivalent hands-on experience in data engineering and backend systems. 4+ years of hands-on experience building and operating production data systems, including database schema design (MS SQL/Postgres preferred), query/index tuning, and ETL/ELT workflows. Strong knowledge for data engineering best practices - pipeline code, data validation, services, monitoring, and tooling. Production experience with a pipeline orchestration platform (e.g. Airflow or Dagster). Hands-on experience with containerization, version control, and CI/CD workflows in a team setting. Demonstrated ownership of operational monitoring, logging, alerting, and incident response for production systems. Experience working with manufacturing, lab, sensor, or other equipment-generated data - bridging the gap between raw machine output and analytics-ready datasets. Track record of maintaining and improving existing systems, not just building greenfield. Hands-on production use of AI coding assistants and/or agentic development tools with a clear point of view on where they help, where they don't, and how to use them responsibly. Desirable Experience: 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. Exposure to MES, ERP, SCADA (Ignition preferred), or other manufacturing-traceability and production-operations platforms. Experience with regulated, quality-sensitive, or audit-driven data workflows (e.g., manufacturing, QC, automotive, aerospace, defense, life sciences). Experience working in early-stage environments where you owned systems end-to-end. Experience building agentic workflows, LLM-backed services, or AI-driven automation into production data or operations systems (RAG over internal data, MCP servers, evals, prompt/tool design). Required Skills: Solid understanding of data systems architecture, including operational databases, pipelines, orchestration, and observability. Excellent critical thinking, problem-solving, and debugging skills, with discipline around documentation and continuous improvement. Self-motivated and able to take full ownership of critical systems, including the unglamorous maintenance work that keeps them running. Proactive in identifying and mitigating reliability, data-quality, and security risks. Strong written and verbal communication, comfortable with both technical deep-dives and stakeholder updates. Comfort working across IT/OT and manufacturing teams to translate production-floor problems into data engineering work. ## Description Design, build, and maintain reliable data systems - orchestration, validation, monitoring, and alerting controls - for production, lab, and equipment data sources. Own infra, schema design, ETL/ELT workflows, CI/CD, monitoring, and data governance. Integrate with manufacturing and lab systems including ceramic production, cell manufacturing, QC, and robotic systems, and the internal applications built around them. Build and maintain back-end services and internal tools - VMs, APIs, data services, and lightweight web apps that surface data to engineering, ops, and executive stakeholders. Design and operate the platform layer: Docker/K8s, git, CI/CD, and backups. Identify incidents, drive to resolution, and lead post-mortems. Minimize single points of failure across the data stack through documentation, cross-training, and engineering for maintainability. Partner with Executive, IT/OT, R&D, 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, refactoring, debugging, documentation, code review, and operational automation. 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