Senior Data Engineer

This role offers the chance to build modern data infrastructure that directly supports how a manufacturing organization operates day to day, giving the work tangible, visible impact. The organization sits in a regulated, mission-driven industry, so the data work here carries real weight beyond typical reporting tasks. The team encourages close collaboration across data architecture, engineering, and business functions, creating an environment where technical and domain expertise are equally valued. There’s solid stability in this position, along with genuine investment in modern data and AI-readiness practices. Leadership is looking to grow their data capabilities over the long term, which means real opportunity for someone who wants to help shape that direction rather than simply maintain existing systems., This is a data engineering position responsible for building and maintaining the pipelines, structures, and documentation that keep organizational data accurate, accessible, and well-governed. The person in this role will design reusable data products, manage the full lifecycle of data integration work, and apply strong engineering discipline - including version control, automated testing, and infrastructure-as-code - to keep deployments repeatable and auditable. It requires close partnership with data architecture, application engineering, and business teams to translate operational data from manufacturing, quality, and supply-chain systems into usable insights. The role also involves preparing datasets to support AI and machine-learning initiatives, working alongside data science teams. It’s a strong fit for someone who enjoys both the technical craft of data engineering and connecting that work to real business outcomes., * Build data pipelines that move information from source systems - including manufacturing execution, ERP, PLM, SCADA/historian, and quality platforms - into target locations, formatting, cleaning, and updating data as business needs evolve.

• Develop and maintain clear documentation of data structures, mappings, and metadata aligned to specific business requirements.
• Design and publish reusable, domain-oriented data products with clear ownership, service-level expectations, and discoverability, standardizing data structures and types across extract-transform-load processes.
• Understand the broader business context and collaborate with data architects, application engineers, integration specialists, and business analysts to ensure the right data reaches authorized users in a timely, secure manner.
• Manage the full lifecycle of ETL/ELT development, applying strong software-engineering practices - version control, automated testing, code review, CI/CD, and infrastructure-as-code - to ensure repeatable, auditable deployments across environments.
• Partner with manufacturing, quality, and supply-chain teams to deliver analytics on operational efficiency, yield, scrap, throughput, engineering-change cycle time, and end-to-end product traceability.
• Prepare AI- and machine-learning-ready datasets, including feature pipelines, dataset versioning, and curated knowledge sources supporting predictive quality, anomaly detection, and retrieval-augmented generation use cases, in partnership with data science and AI teams.

• Bachelor’s degree in Computer Science, Software Engineering, or another related science or engineering discipline; an advanced degree is a plus.
• At least four years of experience in data modeling, data solution development, and data integration.
• Experience integrating data from manufacturing and operational systems such as MES, ERP, PLM, SCADA/historian, LIMS, or QMS is highly preferred.
• Extensive experience with data tools and technologies, particularly Python, SQL, and/or C# .NET.
• Experience analyzing business requirements, planning and executing solutions, and solving complex problems.
• Hands-on experience with a major cloud data stack (such as S3, Glue, EMR, Redshift, Lake Formation, Kinesis, Lambda, and IAM) for building, securing, and operating production data platforms; a relevant cloud data engineering or data analytics certification is highly preferred.
• Experience contributing to a data mesh, data fabric, or digital-thread initiative, including building domain-oriented data products, using a data catalog, and applying federated governance.
• Experience implementing data governance, lineage, and cataloging using cloud-native tools and/or third-party catalog platforms.

Bonus / Soft Skills

• Demonstrated ability to manipulate and aggregate structured and unstructured data from multiple sources through complex queries for analysis and reporting.
• Excellent communication and interpersonal skills, with the ability to convey technical issues, tradeoffs, and results to both technical and business stakeholders.
• Strong ownership and delivery orientation - proactive, detail-oriented, and able to manage multiple priorities against time-sensitive deadlines.
• Knowledge of ETL/ELT tools such as SSIS, Informatica, Talend, dbt, Fivetran, and/or Airflow.
• Working knowledge of DataOps practices and tooling, including Git-based workflows, CI/CD, infrastructure-as-code, automated data testing, and pipeline observability.
• Working knowledge of modern data platform technologies, such as cloud data warehouses or lakehouses, streaming platforms, and data catalog or governance tools used to enable a data mesh.
• Working knowledge and experience with the data models within a major PLM solution.
• Knowledge of modern BI reporting and dashboard tools such as Power BI, Tableau, or Looker.
• Working knowledge of AI/ML data readiness, including feature engineering, dataset versioning and provenance, vector stores, and curating data for retrieval-augmented generation and predictive analytics use cases.
• Working knowledge of manufacturing operations, finance, supply chain, and other functional areas, along with the technology platforms that make up a typical digital thread.

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