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Strong employer brand, broad data engineering skillset requirements, and likely metro hiring increase candidate density.
Deep, specialized data engineering and domain-specific experience required, limiting cross-industry transferability.
Explicit 15-year minimum plus domain-specific cloud, big data and leadership requirements create rigid filters.
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Lead and manage a high-performing data engineering team responsible for designing, building, and operating scalable data solutions enabling analytics and AI use cases within a specific Value Pool (Commercial Effectiveness, Supply Chain Excellence, Innovation, or Corporate Functions).
Own end-to-end delivery of data foundations, including ingestion, transformation, production operation, ensuring data quality, security, governance, and compliance with enterprise standards.
Drive platform optimization, automation (CI/CD, IaC), engineering standards adoption, and collaborate cross-functionally to align data solutions with enterprise data and AI strategies.
Minimum 15 years full-time experience as a data engineer or software engineer.
Proven experience building data solutions for Commercial Effectiveness, Supply Chain Excellence, Innovation, or Corporate Functions data domains.
Bachelor’s degree in Engineering, Mathematics, Statistics, or Computer Science.
Deep experience with Continuous Improvement tools and ETL processes and controls.
Senior technical leader with both hands-on data engineering expertise and managerial accountability for delivery and team development.
Experienced operating in a large enterprise environment delivering complex, compliant, scalable data solutions aligned to business value pools or domains.
Strong background in cloud data platforms (preferably Azure or GCP), big data (Spark), and automation practices (DevOps, CI/CD, IaC).