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Metro location and strong employer brand increase competition, though specialized stack narrows qualified applicants.
Analytics engineering skills transfer well, but required supply-chain expertise increases domain specificity.
Explicit 5+ years and mandatory Snowflake, dbt, Python Dash, and supply-chain experience make screening strict.
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Design and develop interactive, data-driven Python applications integrated with modern data platforms (Snowflake, dbt) to support supply chain, operations, and planning.
Build and maintain scalable data pipelines and curated datasets ensuring high data quality and optimized performance on cloud data warehouses.
Lead collaboration with stakeholders, mentor junior engineers, and uphold best practices in data and application development within supply chain and operations analytics contexts.
5+ years of experience in analytics engineering, data engineering, or full-stack development.
Strong proficiency in SQL, advanced data modeling, and Python application development using frameworks like Dash, Panel, FastAPI, or Flask.
Experience with cloud data warehouses (Snowflake preferred), dbt, Airflow, Git, CI/CD pipelines, and Docker containerization.
Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or related field.
Experienced in supply chain, logistics, manufacturing, or operations analytics environments with ability to translate business requirements into technical solutions.
Skilled in full-stack Python development for data applications focusing on user-centric, scalable self-service analytics solutions.
Comfortable working in global, cross-functional teams with responsibility for mentoring and promoting engineering best practices.