Senior Data Engineer

The confidential employer operates in a regulated, mission-driven manufacturing industry. They are seeking a Senior Data Engineer to build and maintain governed data pipelines and reusable data products, integrate manufacturing and operational data, and prepare datasets for AI and machine-learning initiatives.

Responsibilities:

• 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

Requirements:

• • 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
• • 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
• • 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
• • 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
• • Experience integrating data from manufacturing and operational systems such as MES, ERP, PLM, SCADA/historian, LIMS, or QMS is highly preferred
• • 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