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Tier-1 brand, mid-level generalist data role, metro location, and broad required skillset increase competition.
Core data engineering skills are transferable, but supply-chain domain preference creates moderate industry specificity.
Explicit 6–10 years plus mandatory Databricks, Spark, Python, Airflow and AWS raises filter rigidity.
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Design, build, and maintain scalable ELT/ETL data pipelines using Python, SQL, Databricks, and Apache Spark to support digital customer collaboration and end-to-end supply chain analytics.
Engineer and operate cloud-native data solutions on AWS (including S3, Glue, Lambda, EMR, Redshift) applying lakehouse, Delta Lake, and medallion architecture patterns for performance, scalability, and cost efficiency.
Collaborate with supply chain, analytics, and technology teams to translate business requirements into reliable enterprise data products and mentor junior data engineers.
6–10 years of IT experience with strong focus on data engineering, data platforms, or analytics engineering.
Proven hands-on experience with Databricks, Apache Spark, Python, SQL, Apache Airflow, and AWS data services (S3, Glue, Lambda, EMR, Redshift).
Bachelor’s or Master’s degree in Computer Science, Engineering, Information Technology, Data Engineering, Supply Chain, or related field.
Willingness to travel up to 25% as needed.
Senior-level individual contributor who can independently design and operate production-grade, scalable, fault-tolerant data pipelines in an enterprise environment.
Experienced in integrating and supporting supply chain domains such as demand planning, procurement, manufacturing, logistics, and fulfillment data in analytical solutions.
Strong collaborator skilled at working with global, cross-functional teams across business, analytics, and technology to drive digital supply chain transformation.