Senior Data Engineer at Grain Millers
Eden Prairie, MN 55344, USA -
Full Time


Start Date

Immediate

Expiry Date

23 Nov, 25

Salary

150000.0

Posted On

23 Aug, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Good communication skills

Industry

Information Technology/IT

Description

WHO WE ARE.

Grain Millers, Inc. is a leading manufacturer and merchandiser of whole grain ingredients used in food products around the world. While you may not know our name, you’ve almost certainly enjoyed our products. For almost 40 years, we’ve supplied ingredients to nearly every major food company in North America. With almost 1,100 employees across the U.S. and Canada, our Eden Prairie, MN headquarters supports a growing network of mills, warehouses, and production facilities.

WHY WE NEED YOU.

We’re looking for a Senior Data Engineer to lead the design, development, and optimization of our enterprise data infrastructure. You’ll play a central role in transforming data from ERP, MES, supply chain, and quality systems into trusted, actionable insights that support analytics, reporting, and operational visibility across our manufacturing footprint.
This is a hands-on, strategic role where you’ll combine deep data engineering expertise with an understanding of manufacturing systems and business processes. Your work will power business intelligence, machine learning, and real-time decision-making across Grain Millers.

How To Apply:

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Responsibilities
  • Design, build, and maintain scalable data pipelines using tools such as Azure, Databricks, Airflow, dbt, and Spark.
  • Develop ETL/ELT processes to ingest structured and unstructured data from diverse systems.
  • Partner with architects, engineers, and analysts to design and optimize data models for analytics and operations.
  • Ensure high data quality, availability, and performance across enterprise systems.
  • Collaborate with stakeholders to build dashboards and reporting solutions in BI tools such as Power BI or Tableau.
  • Implement strong data governance practices, including data security, privacy, and compliance.
  • Mentor junior engineers and champion best practices in data engineering.
  • Drive adoption of modern cloud, DevOps, and MLOps practices to support analytics and machine learning initiatives.
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