Senior Data Engineer - Corporate Office (Austin, MN preferred)

Hormel Foods•Austin, MN

1d•Hybrid

About The Position

Hormel Foods Corporation, based in Austin, Minnesota, is a global branded food company with approximately $12 billion in annual revenue across more than 80 countries worldwide. The company is a member of the S&P 500 Index and the S&P 500 Dividend Aristocrats, was named one of the best companies to work for by U.S. News & World Report, one of America’s most responsible companies by Newsweek, recognized by TIME magazine as one of the World’s Best Companies, and has received numerous other awards and accolades for its corporate responsibility and community service efforts. As a Senior Data Engineer, you will lead the Supply Chain Data Engineering team responsible for delivering curated data assets, data pipelines, dimensional models, semantic models, and foundational data capabilities that support planning, procurement, manufacturing, inventory, logistics, and transportation across Hormel Foods. This role plays a key part in advancing our Enterprise Data Foundation by leveraging technologies Google Cloud Platform (GCP), BigQuery, Composer (Airflow), enterprise Python-based ingestion and orchestration capabilities, Incorta, and enterprise semantic layer technologies. This role will help bridge Hormel’s modern cloud data ecosystem and legacy enterprise data platforms while delivering trusted data assets that power Supply Chain analytics and decision-making.

Requirements

Bachelor’s degree in computer science, MIS, engineering, mathematics, or related field, and significant experience supporting enterprise data, analytics, and technology platforms.
7+ years of experience developing and optimizing SQL solutions.
7+ years of experience designing, implementing, and supporting enterprise data warehouse, lakehouse, or modern data platform solutions.
5+ years of experience developing data pipelines and data integration solutions using Python and/or modern ETL/ELT technologies.
Strong experience with dimensional modeling, semantic modeling, and curated analytical data structures, including development and support of enterprise semantic layer solutions.
Demonstrated leadership experience leading technical teams, mentoring engineers, and delivering cross-functional initiatives.
Experience partnering with business stakeholders to translate business requirements into scalable data solutions.
Strong understanding of Supply Chain business processes including planning, procurement, manufacturing, inventory management, logistics, transportation, or warehouse operations.
Experience implementing data quality, monitoring, observability, and operational support practices.
Excellent communication, organizational, problem-solving, and stakeholder management skills.
Applicants must not now, or at any time in the future, require employer sponsorship for a work visa.
Applicants must be authorized to work in the United States for any employer.

Nice To Haves

Experience with Google Cloud Platform technologies including BigQuery, Cloud Storage (GCS), Composer (Airflow), Dataflow, Pub/Sub, and Data Fusion.
Experience utilizing enterprise ingestion and orchestration frameworks to acquire, process, and curate enterprise data assets.
Experience developing enterprise-scale data pipelines and data integration solutions using Python.
Experience with Incorta analytics and data platform technologies.
Experience designing and implementing semantic layer solutions utilizing technologies such as AtScale, Cube, Oracle Analytics Server (OAS), or similar platforms.
Experience designing and supporting curated data products for enterprise analytics and self-service reporting.
Experience with Oracle Enterprise Data Warehouse environments.
Experience with Informatica ETL development.
Advanced SQL and PL/SQL development, performance tuning, and optimization experience.
Experience partnering with Data Governance teams and business data stewards on metadata management, business glossary development, data quality initiatives, observability, critical data elements, and remediation processes.
Experience supporting enterprise data modernization initiatives spanning both cloud-native and legacy data ecosystems.
Experience within food manufacturing, consumer packaged goods (CPG), Supply Chain, logistics, or related industries.
Experience managing and developing technical teams.

Responsibilities

Lead and develop the Supply Chain Data Engineering team responsible for delivering curated data assets, data pipelines, dimensional models, semantic models, and foundational data capabilities supporting planning, procurement, manufacturing, inventory, logistics, and transportation.
Utilize enterprise ingestion, orchestration, and platform capabilities to acquire, integrate, transform, and curate data that supports analytics, reporting, and business decision-making.
Partner with Supply Chain stakeholders, Data Products & Solutions teams, architects, data scientists, and application teams to prioritize initiatives and deliver scalable data solutions that support business objectives.
Design, develop, and support Supply Chain data solutions across modern and legacy platforms, including BigQuery, Composer (Airflow), Python, Incorta, Oracle Analytics Server (OAS), Oracle Enterprise Data Warehouse, Informatica, PL/SQL, and enterprise semantic layer technologies.
Establish and promote engineering best practices, including data modeling standards, code reviews, testing strategies, CI/CD adoption, monitoring, observability, and operational support processes.
Partner with Data Governance teams and business data stewards to implement and maintain data quality rules, metadata cataloging, business glossary definitions, critical data elements, observability capabilities, and data remediation processes across Supply Chain data domains.
Drive modernization initiatives that advance the Enterprise Data Foundation, improving scalability, reliability, developer productivity, and long-term maintainability while reducing technical debt.
Provide technical leadership through architecture reviews, design reviews, mentoring, coaching, and cross-functional collaboration with Data Platform Engineering, Architecture, Infrastructure, Security, Governance, and Integration teams.