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Tier-1 brand, metro location, and popular data-engineer title increase candidate competition.
Requires supply chain/manufacturing analytics and ERP (SAP) familiarity, making background highly domain-sensitive.
Explicit 10+ years requirement plus mandatory Snowflake/dbt/Airflow/Spark/Python skills enforces strict filters.
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Design, build, and optimize scalable data pipelines and enterprise data products to support supply chain, manufacturing, and operational decision-making.
Lead data engineering efforts using cloud technologies like Snowflake, dbt, Airflow, Python, and SQL, ensuring high data quality, reliability, and governance.
Collaborate with stakeholders and analytics teams to enable self-service analytics and accelerate insight generation through automation and scalable data solutions.
10+ years overall IT experience with at least 5+ years in hands-on Data Engineering.
Expert-level SQL skills and strong experience with Snowflake, dbt, Apache Airflow, Python, and Spark/PySpark.
Bachelor's or Master's degree in Computer Science, Information Systems, Engineering, Data Science, or related technical field.
Experience supporting Supply Chain, Manufacturing, Logistics, Operations, or Planning domains.
Experienced in managing large-scale ETL/ELT architectures and performance tuning of Snowflake for scalability and cost-efficiency.
Skilled in applying DataOps, CI/CD, Git workflows, and software engineering best practices in data platform development.
Able to provide technical leadership, mentor junior engineers, and drive platform modernization initiatives in supply chain and operations analytics environments.