Senior Engineer, Data Management

Experteer Overview In this role you will shape Hearst's data and AI roadmap by delivering scalable data pipelines and governance across cloud platforms. You will collaborate with analysts, data scientists, and IT to build reusable data components and enable AI-powered products. You'll work across Snowflake, Microsoft Fabric, and Databricks, applying DevSecOps and Agile practices to ensure secure, high-performance data solutions. This is an opportunity to impact enterprise data strategy and unleash data-driven decision making at scale. Compensation / Benefits * Design and build scalable, secure data pipelines for large volumes of data across systems * Develop APIs and complex database logic to automate data fetch, transformation, and storage * Leverage generative AI tools to accelerate pipeline development, testing, and maintenance * Architect modular data components powering data and AI applications, deployed via containers * Build data solutions across AWS, Azure, and/or GCP * Enforce data quality, security, and governance across cloud environments including IDMC and MDM integrations * Handle regulated data (PHI, PII, PCI) in compliance with regulations * Collaborate with data analysts/scientists/IT to support data/AI products including BI use cases * Provide technical leadership: mentor, review designs, guide on complex issues * Apply DevSecOps and Agile methods (Jira, Scrum/Kanban) to plan and deliver work * Monitor and improve performance, resiliency, and scalability as data grows Tasks * 7+ years of production data pipeline experience with Python and both relational and non-relational databases * Hands-on experience with generative AI in data engineering workflows * Experience designing data solutions on at least one major cloud platform (AWS/Azure/GCP) * Experience with ETL, data modeling, and building reusable modular components * Knowledge of containerization (Docker/Kubernetes) or similar deployment practices * Experience implementing data governance, security, and quality standards (PHI/PII/PCI) * Experience with Informatica IDMC/MDM or comparable data integration tooling * Solid understanding of data structures, algorithms, and software architecture with documentation/testing experience * Comfort collaborating with data analysts/scientists/stakeholders to translate requirements into pipelines * Experience with Agile delivery (Jira, Scrum, Kanban) and DevSecOps practices Key requirements * medical/dental/vision insurance * disability and life insurance * 401(k) * paid holidays and PTO * employee assistance programs

• and quality standards (PHI/PII/PCI) * Experience with Informatica IDMC/MDM or comparable data integration tooling * Solid understanding of data structures, algorithms, and software architecture with documentation/testing experience * Comfort collaborating with data analysts/scientists/stakeholders to translate requirements into pipelines * Experience with Agile delivery (Jira, Scrum, Kanban) and DevSecOps practices Key requirements * medical/dental/vision insurance * disability and life insurance * 401(k) * paid holidays and PTO * employee assistance programs

Experteer Overview In this role you will shape Hearst's data and AI roadmap by delivering scalable data pipelines and governance across cloud platforms. You will collaborate with analysts, data scientists, and IT to build reusable data components and enable AI-powered products. You'll work across Snowflake, Microsoft Fabric, and Databricks, applying DevSecOps and Agile practices to ensure secure, high-performance data solutions. This is an opportunity to impact enterprise data strategy and unleash data-driven decision making at scale. Compensation / Benefits * Design and build scalable, secure data pipelines for large volumes of data across systems * Develop APIs and complex database logic to automate data fetch, transformation, and storage * Leverage generative AI tools to accelerate pipeline development, testing, and maintenance * Architect modular data components powering data and AI applications, deployed via containers * Build data solutions across AWS, Azure, and/or GCP * aaaaak