Senior Data Engineer Pune, All locations, IN - The Hershey Company

Location: Pune, Maharashtra

Summary

The Senior Data Engineer, plays a critical role in designing, building, and operating enterprise grade data products that power analytics, reporting, and AI across Hershey’s business domains. This role ensures data pipelines, models, and foundational components are scalable, performant, governed, and reusable — enabling consistent access to trusted data at enterprise scale.

Senior Data Engineers work closely with Data Product Managers, Architects, Domain SMEs, and Platform Engineering to translate business requirements into robust technical solutions using modern data engineering patterns and cloud-native tooling. The role supports the end-to-end lifecycle of data products, from ingestion and modeling to certification, documentation, and sustainable operations.

This role is foundational to Hershey’s long-term data strategy. By contributing to reusable, governed, and durable data products, the Senior Data Engineer helps establish the enterprise system of record for analytics, enabling efficiency, consistency, and innovation across the company.

What We Are Building for Hershey

This role contributes directly to Hershey’s enterprise data strategy by engineering durable, reusable, and governed data products that become the system of record for analytics and future AI innovation. These data products are designed to scale across domains, reduce duplication, increase trust, and accelerate enterprise decision-making.

By applying engineering rigor, governance-by-design practices, and modern cloud-native architecture, the Senior Data Engineer helps transition Hershey from one-off data solutions to long‑lived, production-grade assets that fuel efficiency, growth, and innovation across the enterprise.

Major Duties & Responsibilities

1. Data Product Engineering & Delivery

Partner with business stakeholders to translate business requirements into technical designs and acceptance criteria.

Design, build, and maintain scalable and reusable data pipelines and transformations using Azure and Databricks.

Implement robust ingestion frameworks, standardized patterns, and repeatable workflows aligned to enterprise engineering practices.

Build and optimize physical and semantic data models supporting analytics, reporting, and AI/ML use cases.

2. Technical & Architectural Implementation

Apply best practices related to performance tuning, cost optimization, security, and reliability.

Ensure alignment with enterprise architecture, including shared infrastructure, canonical models, and platform standards.

Implement efficient data processing patterns (e.g., Delta Lake, medallion architecture, orchestration frameworks).

3. Governance, Quality, & Operations

Embed governance-by-design principles including lineage, metadata, documentation, and certification stan