The Associate Manager Data Analytics is the connective tissue across Hershey’s data landscape—the person who understands how data relates across domains, traces it back to its source, and delivers trusted answers to the questions that drive business decisions. This is not a dashboarding or visualization role. This is a hands-on-the-data role for someone who thinks in joins, speaks in SQL, and gets energized by making sense of complexity.
Reporting to Data Product Managers within the Data Analytics team, this analyst works day-to-day with Data Product Owners to validate that data products are built on accurate, well-understood foundations. They partner with the Data Governance team to uphold definitions, standards, and quality. They support Data Engineers by translating business questions into precise technical requirements. And they sit alongside domain teams and business users to ensure that when Hershey makes a decision with data, the data underneath is right.
What We Are Building for Hershey
Hershey’s data products span retail execution, supply chain planning, commercial analytics, and consumer insights—and the most valuable answers live at the intersections. Why did trade promotion ROI shift? The answer connects promotion spend data to shipment data to retailer sell-through. Where are we losing shelf availability? The answer threads inventory, logistics, and store-level POS together. This role exists because those cross-domain connections don’t happen by accident—they require someone with sharp analytical skills and deep curiosity who can trace data across systems, validate the logic, and deliver answers the business can trust. Every dashboard, every ML model, and every executive decision at Hershey is only as good as the data underneath it. This analyst makes sure that data holds up.
Major Duties & Responsibilities
Data Investigation & Question Resolution
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Receive business questions from Data Product Managers, Data Product Owners, and domain teams; decompose them into data requirements, trace across source systems, and deliver validated, data-backed answers.
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Conduct exploratory analysis across datasets to uncover patterns, anomalies, and root causes that inform data product decisions.
Data Connectivity & Relationship Mapping
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Understand and document how data entities relate across Hershey’s domains—how a promotion ties to a shipment, how a demand forecast connects to production planning, how consumer data links to commercial outcomes.
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Work with Data Product Owners to validate that the data relationships underlying their products are correct, complete, and fit for purpose.
Data Validation & Quality Analysis
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Profile datasets, validate business rules, reconcile data across sources, and identify quality issues that affect downstream data products and analytics.
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Surface findings to the Data Governance team—flagging definition inconsistencies, undocumented transformations, and quality gaps to strengthen enterprise data standards.
Governance Alignment & Standards Support
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Partner with the Data Governance team to validate business term definitions against actual data behavior, contribute to data dictionaries and catalogs, and ensure analytical work aligns with governance policies.
Requirements & Cross-Team Collaboration
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Translate what Data Product Owners need into specifications that Data Product Managers can prioritize and Data Engineers can build—bridging business intent and technical implementation.
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Support domain teams and business users by providing analytical expertise on data structure, availability, and meaning across Hershey’s data estate.
Required Knowledge, Skills, and Abilities
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SQL & Data Analysis: Solid SQL skills for querying, joining, and analyzing data across multi-source environments. Comfort writing queries that span domains and validate data relationships.
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Analytical Reasoning & Data Connectivity: Ability to take a broad business question, break it into data requirements, and work methodically toward a validated answer. Understanding of how datasets relate across systems and where data connections can break down.
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Communication & Collaboration: Ability to explain data findings clearly to both technical and business audiences; comfort working across Data Product Owners, Governance, Engineering, and domain teams.
Preferred Skills
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Python for data analysis (pandas, notebooks) and basic automation of analytical workflows.
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Familiarity with cloud data platforms, particularly Azure and Databricks.
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Exposure to data governance concepts: metadata management, data catalogs, business glossaries, and data quality frameworks.
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Understanding of data modeling concepts (dimensional modeling, entity-relationship design) and how they affect analytical work.
Experience & Education
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Bachelor’s degree in Data Science, Information Systems, Computer Science, Statistics, Business Analytics, or a related analytical field.
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2–4 years of experience in data analysis, analytics engineering, or a data-focused role working with production datasets.
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Demonstrated ability to write complex SQL, work with relational data across multiple sources, and reason about how data connects across systems.
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