Senior Data Engineer

Position Summary

This position is responsible for managing and organizing data to support all business processes and achieve corporate and departmental goals. The role entails identifying trends and communicating insights clearly to ensure data is properly used. Core activities include troubleshooting data issues, assisting the data architect to develop, align, and maintain architectures with business requirements, and implementing strategies to acquire high‑quality data in a timely, accurate, reliable, and efficient manner.

The successful candidate will work closely with applications and integration engineers to meet compliance and governance needs.

• Develop data pipelines from various sources to target locations (MES, ERP, PLM, SCADA/historian, quality systems) to support the enterprise Digital Thread; format, clean, and update data as needed.
• Maintain accurate data structure and mapping documentation, including metadata aligned to business requirements.
• Design and publish domain‑oriented, reusable data products aligned to a data mesh model with clear ownership, SLAs, and discoverability.
• Collaborate with the data architect, application engineers, integration specialists, business analysts, and other technical experts to deliver content to authorized users in a timely, secure, and effective manner.
• Manage the full life‑cycle development for current ETL/ELT deployments, applying software‑engineering practices (Git, automated testing, code review, CI/CD, IaC such as Terraform or Cloud Formation).
• 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 use cases, in partnership with data science and AI teams.

• Bachelor’s degree in Computer Science, Software Engineering, or related discipline (advanced degree preferred).
• Minimum of four years experience in Data Modeling, Data Solution Development, and Data Integration.
• Experience integrating data from manufacturing and operational systems (MES, ERP, PLM, SCADA/historian, LIMS, or QMS).
• Extensive experience with data science tools/technologies, particularly Python, SQL, and/or C# .NET.
• Hands‑on experience with the AWS data stack (S3, Glue, EMR, Redshift, Lake Formation, Kinesis, Lambda, IAM); AWS Certified Data Engineer or AWS Certified Data Analytics certification is highly preferred.
• Experience contributing to data mesh, data fabric, or Digital Thread initiatives—including building domain‑oriented products and employing federated governance.
• Experience implementing data governance, lineage, and cataloging on AWS (e.g., AWS Glue Data Catalog, Lake Formation, Collibra, Alation, Atlan, or Open Metadata) is highly preferred.

• Proficiency in manipulating and aggregating structured and unstructured data from multiple sources, building complex queries for analysis and reporting.
• Excellent communication and interpersonal skills, with the ability to convey technical issues, trade‑offs, and results to stakeholders.
• Strong ownership and delivery orientation—proactive, detail‑oriented, and capable of managing multiple priorities against deadlines.
• Knowledge of ETL/ELT process tools (SSIS, Informatica, Talend, dbt, Fivetran, Airflow) is highly preferred.
• Working knowledge of Data Ops practices and tooling (Git, CI/CD, IaC, automated data testing, pipeline observability).
• Working knowledge of modern data platform technologies (Snowflake, Databricks, Big Query, Kafka, Kinesis, data catalog/governance tools).
• Experience with Siemens Teamcenter PLM data models is highly preferred.
• Knowledge of modern BI reporting/dashboard tools (Power BI, Tableau, Looker) is highly preferred.
• Understanding of AI/ML data readiness concepts, vector stores, and data curation for RAG and predictive analytics.
• Working knowledge of manufacturing…