Back
Senior, specialized enterprise data engineering leadership reduces applicant pool but attracts experienced candidates, moderate competition.
Core data engineering skills are transferable, but commercial value-pool and GxP experience increase industry specificity.
Explicit 15-year requirement, leadership responsibility, and specific cloud/big-data tooling make shortlisting highly strict.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Lead and manage a data engineering team to design, build, and operate scalable data solutions supporting analytics and AI across assigned Commercial Effectiveness or related Value Pools.
Own the full data engineering lifecycle within the domain, including prioritization, capacity management, and ensuring solutions meet performance, security, quality, and governance standards.
Drive adoption of engineering standards, automation (CI/CD, IaC), platform optimization, and provide hands-on technical leadership and mentoring to elevate engineering capabilities.
Minimum 15 years full-time experience as a data or software engineer.
Bachelor's degree in Engineering, Mathematics, Statistics, or Computer Science.
Proven experience building data solutions for Commercial Effectiveness, Supply Chain Excellence, Innovation, or Corporate Functions Value Pools and their data domains.
Deep experience with ETL processes, Continuous Improvement tools; no explicit mention of notice period.
Experienced leader capable of managing technical teams while providing hands-on engineering leadership and making authoritative technical decisions.
Strong background in enterprise-scale, cloud-based data engineering including big data platforms (Spark), cloud services (Azure/GCP), and modern data architectures (streaming, event-driven).
Skilled in integrating data quality, security, governance by design, and driving automation and operational excellence with Agile and DevOps methodologies.