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Strong employer brand, metro location, and a common Senior Data Engineer title increase competition.
Data engineering skills are transferable but preferred supply-chain domain experience raises sensitivity moderately.
Explicit 10+ years requirement plus mandatory Snowflake, dbt, Airflow, Python, Spark, and supply-chain experience increases strictness.
Design, build, and optimize scalable data pipelines and enterprise data products to support supply chain, manufacturing, and operational decision-making.
Develop and maintain data models, implement automated data quality frameworks, and optimize cloud data platforms (Snowflake) for performance and cost efficiency.
Collaborate with business stakeholders and analytics teams to deliver trusted, self-service analytics solutions and provide technical leadership including mentoring and platform modernization.
10+ years IT experience with at least 5 years hands-on Data Engineering.
Expert-level SQL skills, strong Python programming, and experience with Snowflake, dbt, Apache Airflow, Spark/PySpark, and ELT architectures.
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.
Proven ability to lead technical design and implementation of large-scale, cloud-native data engineering solutions in supply chain or operational contexts.
Experience working in cross-functional teams with business stakeholders and analytics professionals to deliver enterprise-scale data products and self-service platforms.
Strong focus on data quality, automation, CI/CD, and operational support in a hybrid work model aligned with EST morning overlap.