Data Engineer - Global Manufacturing Digitalization

Siemens•Wendell, NC

1d•$83,966 - $143,942•Hybrid

About The Position

Siemens is hiring an Associate Manufacturing Data Engineer for its Electrification & Automation Business Unit, based in Wendell, North Carolina. The role supports Siemens’ global manufacturing network by creating and maintaining data infrastructure and analytics solutions that improve production transparency and operational decision-making. The position partners with manufacturing, operations, and technology teams, translating business needs into scalable data solutions and validating results with stakeholders. It contributes to product traceability, process performance analysis, and manufacturing analytics, while supporting data governance, reliability, maintainability, and global reuse.

Requirements

Bachelor’s degree in Computer Science, Data Engineering, Software Engineering, Information Systems, Industrial Engineering, or a related technical field; equivalent practical experience is also considered.
At least 1 year of experience in data engineering, analytics, software development, or a related technical discipline.
Working knowledge of SQL demonstrated through coursework, internships, projects, or professional experience.
Foundational understanding of data modeling, data warehousing, ETL/ELT processes, and modern data engineering concepts.
Experience or familiarity with Python and data transformation techniques.
Understanding of data quality practices, testing methodologies, and troubleshooting approaches.
Strong analytical, problem-solving, and critical-thinking abilities.
Willingness and ability to travel worldwide up to 20%.
Legally authorized to work in the United States on a continual and permanent basis without company sponsorship.

Nice To Haves

Ability to communicate effectively and collaborate with technical and business stakeholders.
Interest in learning cloud-based data platforms, manufacturing processes, and modern analytics technologies.
Strong organizational skills and ability to document technical solutions and processes clearly.
Experience with Snowflake or another cloud-based data warehouse platform.
Familiarity with dbt, version control tools such as Git, and automated testing frameworks for data transformations.
Experience with AWS services for cloud-based data ingestion, storage, or processing.
Familiarity with AWS IoT, Industrial IoT, or manufacturing data environments.
Knowledge of Kafka, event-driven architectures, or real-time streaming data pipelines.
Experience with Power BI, Grafana, Tableau, or other business intelligence and visualization tools.
Understanding of semantic models, dimensional modeling, governed analytics, or KPI frameworks.
Exposure to manufacturing analytics, production systems, or operational technology environments.
Internship, co-op, academic project, or early-career experience involving data engineering or analytics solutions.
Spanish language proficiency.

Responsibilities

Build and maintain data pipelines that collect, transform, and integrate manufacturing and operational data from multiple source systems.
Develop cloud-based data models, tables, and schemas in Snowflake for scalable reporting, analytics, and manufacturing insights.
Design, implement, and test data transformation logic using SQL, Python, dbt, and data engineering best practices.
Monitor and improve data quality through validation, troubleshooting, testing, and collaboration with source-system owners.
Support reusable semantic and analytical models aligned with business definitions, KPIs, and performance metrics.
Partner with manufacturing, operations, and technology teams to deliver solutions that improve production transparency and operational decision-making.
Contribute to product traceability, process performance analysis, and manufacturing analytics initiatives.
Document data pipelines, models, interfaces, and transformation logic to support maintainability, governance, and global reuse.
Translate business requirements into scalable data solutions and validate results with stakeholders.
Participate in continuous improvement focused on data architecture, performance optimization, reliability, and governance.
Travel worldwide up to 20% to support global manufacturing facilities.