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# Data and Analytics Engineer - Global Manufacturing

- **Company:** Siemens AG
- **Location:** Wendell, NC, United States
- **Experience:** Expert
- **Salary:** $102,459.0 - $175,644.0
- **Contract:** Permanent contract
- **Skills:** Business Analytics Applications, Data Analysis, Cloud Computing, Cloud Database, Information Systems, Data Architecture, Information Engineering, Database Development, Distributed Data Store, Python (Programming Language), Power BI, Data Mesh, Software Engineering, Tableau (Software), Technical Data Management Systems, Data Ingestion, Snowflake, Grafana, Event Driven Architecture, Information Technology, Data Analytics, Performance Monitor, Apache Kafka, Stream Processing, Data Pipelines
- **Published:** October 27, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=c186fec8e50d8797

## About the Role

* Bachelor's degree in Data Engineering, Computer Science, Software Engineering, Information Systems, Industrial Engineering, or a related technical field; equivalent practical experience will also be considered.
 * 5+ years of experience developing and supporting data pipelines, cloud-based data solutions, or enterprise analytics platforms.
 * Experience with SQL development skills and proficiency in at least one programming language such as Python
 * Hands-on experience with Snowflake and demonstrated ability to build, maintain, test, and troubleshoot data models and transformation pipelines.
 * Experience implementing data quality validation, monitoring, testing, and troubleshooting processes.
 * Experience translating manufacturing or operational business requirements into meaningful metrics, KPIs, and reporting solutions.
 * Experience creating operational dashboards, reports, and performance visualizations that support business decision-making.
 * Willingness and ability to travel up to 20% to support U.S. manufacturing facilities.
 * Legally authorized to work in the United States on a continual and permanent basis without company sponsorship., * 5+ years of experience in data engineering, manufacturing analytics, Industrial IoT, or industrial digitalization environments.
 * Experience working with manufacturing, production, time-series, machine, or operational technology data.
 * Experience with Kafka, event-driven architectures, or real-time data streaming technologies.
 * Knowledge of product traceability solutions and manufacturing genealogy data models.
 * Experience integrating edge and cloud-based technologies within manufacturing environments.
 * Familiarity with semantic modeling concepts, governed analytics frameworks, and reusable KPI definitions.
 * Experience with Grafana, Power BI, Tableau, or other enterprise business intelligence platforms.
 * Understanding of data mesh, modern data architectures, or distributed data ownership principles.

## Description

* Building and maintaining scalable data pipelines for our US manufacturing network that collect, transform, and deliver manufacturing data for operational reporting, analytics, and continuous improvement initiatives.
 * Developing and supporting cloud-based data models that accurately represent production processes, assets, events, and manufacturing performance.
 * Collaborating with Industrial IoT and connectivity engineers to ensure reliable data ingestion, validation, monitoring, and troubleshooting across manufacturing systems.
 * Defining business terms, relationships, and KPI calculations in partnership with manufacturing, quality, and operational stakeholders.
 * Developing reusable semantic and analytical models that establish consistent reporting standards and trusted operational insights.
 * Creating dashboards, visualizations, and analytical solutions that help production teams identify losses, monitor performance, and drive measurable improvements.
 * Supporting product traceability initiatives by developing data models and retrieval capabilities that improve manufacturing visibility and quality investigations.
 * Diagnosing and resolving data quality, integrity, and performance issues across multiple systems and data sources.
 * Documenting data architectures, schemas, KPI definitions, semantic models, and governance practices to support long-term scalability and adoption.
 * Working closely with business users to translate analytical findings into actionable improvement plans and operational decision-making.