Data Engineer

Job Location: Dallas, TX
This position can be based in Dallas in our Frisco office or at 19E in Hershey, PA

Summary:
The Data Engineer, Data Products supports the design, build, and operation of data pipelines and models that power Hershey's enterprise data products. Working within established architectures and with guidance from Senior Data Engineers and Architects, this role develops reliable, scalable data transformations that enable analytics, reporting, and AI across Hershey's business domains.
Data Engineers collaborate closely with Senior Data Engineers, Architects, Platform Engineering, and domain teams to translate defined business requirements into high quality technical solutions using modern cloud-native tools such as Azure and Databricks. They contribute to key stages of the data product lifecycle, including ingestion, transformation, modeling, documentation, and operational support.
By helping build reusable and well engineered data assets, the Data Engineer strengthens Hershey's broader data strategy-improving data quality, consistency, and accessibility while supporting the development of trusted, long-term data products.

What We Are Building for Hershey
This role contributes to Hershey's enterprise data strategy by engineering reusable, governed data assets that scale across domains and reduce duplication. By applying engineering discipline and governance-by-design, Data Engineers help evolve one-off solutions into durable, production-grade data products that improve trust and speed of decision-making.

Major Duties & Responsibilities
Data Product Engineering & Delivery:
* Build and maintain scalable data ingestion and transformation pipelines on Azure and Databricks; develop curated/semantic models for analytics and AI.
* Translate requirements into technical designs and acceptance criteria with Product Managers.
Technical & Architectural Implementation
* Apply best practices for performance, cost, security, and reliability; follow enterprise standards and shared infrastructure.
* Implement efficient patterns such as Delta Lake, medallion architecture, and orchestration frameworks.
Governance, Quality & Operations
* Embed governance-by-design including metadata, lineage, documentation, certification, and automated data quality checks.
* Operate and troubleshoot production pipelines; contribute improvements to standards and automation.
Collaboration Across Domains
* Partner with domain teams to validate definitions and ensure products meet business needs.
* Work with Platform Engineering on pipeline frameworks and optimization; coordinate with Data Ops & Enablement on documentation and certification.

Minimum Knowledge, Skills, and Abilities
* Data Engineering & Pipelines: ETL/ELT development, distributed processing, and workflow optimization.
* Cloud & Platforms: Hands-on with Databricks and Azure (preferred) or AWS data platforms.
* Programming & Development: Proficient in Python and SQL; experience with modular coding, APIs, automation, and source control.
* Data Modeling & Quality: Dimensional/semantic modeling; familiarity with data quality, metadata, lineage, and catalog tools.
* Collaboration & Communication: Ability to translate business needs into technical solutions and communicate effectively across teams.

Experience & Education
* Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or related field
* 2-5 years in data engineering or analytics engineering roles.
* Experience building pipelines and models in Azure/Databricks or equivalent AWS tooling.
* Working knowledge of SQL and NoSQL data stores (PostgreSQL, MySQL, MongoDB).

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