Senior Engineer, Data Management

Hearst is one of the nation's largest diversified media, information, and services companies, with more than 360 businesses spanning cable networks (A&E, HISTORY, ESPN), financial data (Fitch Group), healthcare information (Hearst Health), transportation, and digital services (iCrossing, KUBRA). Our strength comes from the range of backgrounds, disciplines, and perspectives our people bring together.

Hearst Technology Services is looking for a Senior Data Engineer to help shape our data and AI technology roadmap. You'll own the full lifecycle of complex data projects — from analysis and design through development and production support — building pipelines and integrations that move data securely and reliably across cloud platforms. You'll also help design the tooling that extracts, transforms, and governs data from both internal systems and external sources, and act as a technical resource for engineers, analysts, and data scientists across the business.

• Design and build scalable, secure pipelines that ingest, transform, and move large volumes of structured and unstructured data across systems.

• Build APIs and complex database logic to automate the fetch, transformation, and storage of data in multiple formats.

• Use generative AI tools to accelerate the development, testing, and maintenance of pipelines across Snowflake, Microsoft Fabric, and Databricks.

• Architect and maintain modular, reusable components that power larger data and AI applications, deployed via containerized workflows (Docker/Kubernetes) where applicable.

• Design, build, and maintain data solutions across AWS, Azure, and/or Google Cloud.

• Implement data quality, security, and governance standards consistently across cloud environments, including integrations between platforms like Informatica IDMC and MDM.

• Securely handle PHI, PII, and PCI data in line with regulatory and internal compliance requirements.

• Partner with data analysts, data scientists, and IT operations to build tools and pipelines that power new data and AI products — including downstream reporting and BI use cases.

• Provide technical leadership on projects: mentor engineers, review designs, and offer guidance on complex technical or production issues.

• Apply DevSecOps practices and Agile methodologies (Jira, Scrum/Kanban) to plan and deliver work.

• Monitor and improve application performance, resiliency, and scalability as data volumes grow.

Required

• 7+ years building production data pipelines, with strong Python (or equivalent) and both relational and non-relational database experience.

• Hands-on experience with generative AI tools applied to data engineering workflows.

• Experience designing and maintaining data solutions on at least one major cloud platform (AWS, Azure, or GCP).

• Experience with ETL tools, data modeling, and building reusable, modular components for complex systems.

• Working knowledge of containerization (Docker/Kubernetes) or similar deployment practices.

• Experience implementing data governance, security, and quality standards, ideally including regulated data (PHI/PII/PCI).

• Experience with Informatica IDMC/MDM or comparable data integration and master-data tooling.

• Solid grounding in data structures, algorithms, and software architecture, with experience documenting and testing complex systems.

• Comfort working directly with data analysts, data scientists, and business stakeholders to translate requirements into pipelines and products.

• Experience with Agile delivery (Jira, Scrum, or Kanban) and DevSecOps practices.

Nice to Have

• Prior experience leading a small team of data engineers/analysts.