Data Engineer

Responsibilities: of the Data Engineer include: Develop and maintain ETL/ELT pipelines that integrate data from MES, ERP, PLM, SCADA/Historian, Quality, and other enterprise systems. Design reusable, domain-oriented data products while maintaining data models, metadata, mappings, and governance standards. Collaborate with architects, engineers, analysts, and business teams to deliver secure, scalable, and reliable data solutions. Manage the full lifecycle of data platform development utilizing Git, CI/CD, automated testing, Infrastructure-as-Code, and DataOps best practices. Support analytics, Digital Thread initiatives, and AI/ML-ready datasets for predictive analytics, anomaly detection, and RAG use cases. Qualifications: for the Data Engineer include: Bachelor's Degree in Computer Science, Software Engineering, or a related engineering discipline with 4+ years of experience in data modeling, data integration, and data solution development. Strong experience with Python, SQL, and/or C# .NET, including processing and analyzing structured and unstructured data. Hands-on experience with AWS data technologies including S3, Glue, EMR, Redshift, Lake Formation, Kinesis, Lambda, and IAM. Experience building ETL/ELT pipelines and integrating data from manufacturing systems such as MES, ERP, PLM, SCADA/Historian, LIMS, or QMS environments. Experience with DataOps practices, Git-based workflows, CI/CD pipelines, Infrastructure-as-Code, automated testing, and enterprise data governance. Experience supporting Data Mesh, Data Fabric, Digital Thread, data cataloging, lineage, metadata management, and domain-oriented data products. Knowledge of modern data platforms and tools including Snowflake, Databricks, Kafka, Airflow, dbt, Fivetran, Power BI, Tableau, Looker, and cloud-based data architectures. Strong communication, problem-solving, and stakeholder engagement skills; AWS Data Engineer/Data Analytics certifications, Siemens Teamcenter PLM, and AI/ML data readiness experience are highly preferred.