Senior Data Engineer

POSITION SUMMARY

This position is responsible for managing and organizing data to support all business processes to achieve corporate and departmental goals. This position is responsible for identifying trends and communicating these trends clearly to others in the organization to ensure data is properly used. Core activities include troubleshooting data issues, and assisting the data architect to develop, align, and maintain architectures with business requirements. In addition, this position is expected to implement strategies to acquire high quality data, timely, accurately, reliably, and efficiently by developing the right data processes with the applications and integration engineers to meet all necessary compliance and governance needs.

DUTIES AND RESPONSIBILITIES:

• Develop data pipelines from various data sources to target locations — including MES, ERP, PLM, SCADA/historian, and quality systems — to support the enterprise Digital Thread, including formatting, cleaning, and updating data as the business needs.
• Develop and maintain accurate data structure and mapping documentation, including metadata aligned to specific business requirements.
• Design and publish domain-oriented, reusable data products aligned to a data mesh model with clear ownership, SLAs, and discoverability — standardizing data structure and types across ETL/ELT processes.
• Understand the big picture, set the right scope, and collaborate with the data architect, application engineers, integration specialists, business analysts, and other technical experts to ensure adequate content delivery to authorized users in a timely, effective, and secure manner.
• Manage the full life cycle development for the current ETL/ELT deployments, applying software-engineering practices to data pipelines — version control (Git), automated testing, code review, CI/CD, and Infrastructure-as-Code (e.g., Terraform, CloudFormation) — to ensure repeatable, auditable deployments across environments.
• Partner with manufacturing operations, quality, and supply chain teams to deliver analytics on OEE, yield, scrap, throughput, engineering-change cycle time, time-to-release, and end-to-end product genealogy/traceability supported by the Digital Thread.
• Prepare AI/ML-ready datasets — including feature pipelines, dataset versioning, and curated knowledge sources for predictive quality, anomaly detection, and retrieval-augmented (RAG) use cases — in partnership with data science and AI teams.

EXPERIENCE AND QUALIFICATIONS:

• Bachelor’s degree in Computer Science, Software Engineering, or other re