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Tier-1 brand, popular Data Engineer title, metro location, and broad skills requirements increase applicant competition.
Core data engineering skills are transferable across industries, but supply-chain domain experience raises specificity.
Explicit 10+ years requirement plus mandatory Snowflake, dbt, Airflow, Python, and Spark skills make filters strict.
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Design, build, and optimize scalable, reliable data pipelines and enterprise data products to support supply chain, manufacturing, and operations analytics.
Develop and maintain data models, automate data quality/testing, and optimize Snowflake workloads for performance and cost-efficiency.
Collaborate with business and analytics teams to enable trusted, self-service data access and drive data-driven decision making.
10+ years overall IT experience with at least 5+ years in hands-on data engineering.
Proficiency in SQL, Python, and experience with Snowflake, dbt, Apache Airflow, Spark/PySpark, and pipeline orchestration frameworks.
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.
Deep expertise in enterprise-scale ETL/ELT design and cloud data platforms, especially Snowflake, with strong data modeling skills.
Demonstrated ability to lead technical standards, implement data quality and DevOps best practices, and mentor junior engineers.
Experience integrating data across enterprise systems, supporting supply chain and operational decision-making environments, with stakeholder collaboration experience.