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Brand and Bengaluru metro increase competition, but seniority and niche skills reduce applicant density.
Medium—senior data engineering skills are transferable but commercial-effectiveness domain experience is preferred.
High—explicit 15-year requirement and strong domain and technical expectations.
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Lead and manage a high-performing data engineering team to design, build, and operate scalable data solutions for assigned Value Pools such as Commercial Effectiveness.
Own full data engineering lifecycle including ingestion, transformation, production operation and embed data quality, security, and governance controls.
Drive platform efficiency and automation, operational excellence, and align engineering delivery with enterprise data and AI strategy.
Minimum 15 years full-time experience as a data engineer or software engineer.
Proven experience building data solutions in domains like Commercial Effectiveness, Supply Chain Excellence, Innovation, or Corporate Functions.
Bachelor's degree in Engineering, Mathematics, Statistics, or Computer Science.
Deep experience with ETL processes, continuous improvement tools, and embedding data controls.
Experienced leader with hands-on technical depth in data engineering at enterprise scale, managing end-to-end delivery and quality.
Strong domain knowledge in building data foundations for specific business Value Pools and translating analytics/AI needs into robust data assets.
Skilled in cloud platforms (preferably Azure or GCP), big data technologies (Spark, Databricks), automation, and DevOps practices to drive stable, scalable solutions.