Sr Platform Engineer

The Hershey Company•Dallas, TX

2d•$115,520 - $144,400•Hybrid

About The Position

The Sr. Platform Engineer role combines end-to-end data pipeline delivery with the platform engineering capabilities that power Hershey’s enterprise data products. As a fully autonomous contributor, you will own features, datasets, pipelines, and shared platform components of moderate complexity across development, test, and production. Given defined business requirements, you will independently design, build, deploy, and operate reliable solutions while resolving technical ambiguity with minimal guidance. You will help turn one-off solutions into reusable templates, guardrails, and automated workflows that improve reliability, observability, cost transparency, and developer experience. Partnering with Senior Data Engineers, Solution Architects, the Cloud COE, Security, Data Science, and Data Product teams, you will help keep Hershey’s data platform secure, scalable, governed, and easy to use.

Requirements

3+ years of experience in data engineering, platform engineering, data integration, or a related technical field.
Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or a related field; equivalent experience considered.
Experience with Databricks and Azure data platform services, including environment management, access controls, secrets management, and platform configuration.
Proficiency in Python and SQL for automation, pipeline development, and troubleshooting; familiarity with APIs, scripting, and Git.
Experience implementing CI/CD pipelines in Azure DevOps and supporting release management best practices.
Knowledge of monitoring, alerting, incident management, data quality, and root-cause analysis.
Infrastructure-as-code experience (Terraform preferred); familiarity with MLflow and MLOps practices is a plus.

Nice To Haves

Infrastructure-as-code experience (Terraform preferred)
Familiarity with MLflow and MLOps practices

Responsibilities

Own End-to-End Data & Platform Delivery: Design, build, deploy, and support data pipelines, data models, and platform components, leveraging Databricks and Azure services. Translate business requirements into scalable, production-ready solutions across development, test, and production environments.
Improve Performance, Reliability & Observability: Optimize queries, pipelines, clusters, storage, and compute resources to enhance performance and cost efficiency. Implement monitoring, alerting, dashboards, data quality controls, and operational runbooks. Track key operational metrics, including availability, performance, deployment frequency, and recovery time, to drive continuous improvement.
Resolve Complex Data & Platform Issues: Troubleshoot and resolve data, integration, performance, and platform issues. Conduct root-cause analysis and implement lasting solutions to improve platform stability and data integrity.
Strengthen Governance, Security & MLOps: Implement governance, security, and compliance standards through policies, controls, access management, secrets management, and platform guardrails. Enable MLOps capabilities, including MLflow standards, deployment patterns, and automation in partnership with Data Science and Engineering teams.