Staff Data Engineer, Data Products

The Hershey Company•Dallas, TX

1d•Onsite

About The Position

The Staff Data Engineer, Data Products, plays a critical role in designing, building, and operating enterprise-grade data products that power analytics, reporting, and AI across Hershey’s business domains. In this role, you will help advance Hershey’s enterprise data strategy by developing scalable, governed, and reusable data products that serve as trusted systems of record for analytics and future AI innovation. Working closely with Data Product Managers, Architects, Domain SMEs, and Platform Engineering teams, you will transform business requirements into modern cloud-native data solutions that drive efficiency, consistency, and enterprise-wide decision-making.

Requirements

5+ years of experience in data engineering, analytics, data platforms, or related technical roles.
Hands-on experience with ETL/ELT development, distributed data processing, and enterprise-scale data pipelines.
Experience with Databricks and cloud platforms such as Azure (preferred) or AWS.
Strong proficiency in Python, SQL, APIs, source control, automation, and modern software development practices.
Experience designing dimensional and semantic data models for analytics and reporting.
Familiarity with data governance, metadata management, lineage, data quality, and cataloging frameworks.
Strong collaboration and communication skills with the ability to translate complex business requirements into technical solutions.
Bachelor’s or Master’s degree in Data Science, Computer Science, Engineering, Information Systems, or a related field.
Experience with one or more programming languages such as Python, Java, C#, JavaScript, or C/C++.
Working knowledge of SQL and NoSQL technologies such as PostgreSQL, MySQL, or MongoDB.

Responsibilities

Design, build, and maintain scalable data pipelines and transformations using Azure, Databricks, and modern data engineering frameworks.
Partner with business stakeholders to translate requirements into technical designs, solutions, and acceptance criteria.
Develop and optimize physical and semantic data models supporting analytics, reporting, and AI/ML use cases.
Implement ingestion frameworks, reusable engineering patterns, and data workflows aligned with enterprise standards.
Apply best practices for performance, scalability, cost optimization, security, and reliability.
Support modern architectural patterns including Delta Lake, medallion architecture, orchestration frameworks, and cloud-native services.
Embed governance-by-design principles, including data lineage, metadata management, documentation, certification, and monitoring.
Implement automated data quality checks and controls to ensure trusted, accurate, and complete data products.
Collaborate with business domain teams, platform engineering, and data operations partners to ensure successful delivery, adoption, and ongoing optimization of enterprise data products.