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Popular mid-level data engineer title with broad tech stack and 3-5 year range increases competition.
Core data engineering skills (Snowflake, SQL, Python) are broadly transferable across industries despite supply-chain preference.
Explicit 3-5 years plus mandatory Snowflake, Azure, SQL, Python and ETL requirements raise filtering strictness.
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Lead development and continuous improvement of digital data products and advanced analytics supporting Global Supply Chain.
Manage and optimize data infrastructure including Snowflake, SQL databases, and enterprise semantic models ensuring IT security compliance.
Drive adoption of modern data architecture, data governance, and best practices across platforms like Power BI, Azure, and on-prem systems using Agile methodologies.
3-5 years experience in data engineering, analytics, or business intelligence roles.
Proficiency with Snowflake cloud data warehouse, Azure platform (DBT, SQL Server, Logic Apps, Data Factory, Power BI), advanced SQL, and Python programming.
Strong skills in ETL/ELT pipelines, dimensional data modelling, master data management, data governance, and Agile/Scrum project management (at least 2 years).
Bachelor's or Master's degree preferred in Computer Science, IT, or Engineering.
Experienced in applying modern data architectures and tooling (Snowflake, Azure, DBT) in enterprise environments with strong project ownership.
Skilled at collaborating across cross-functional teams and mentoring peers on data governance and management frameworks.
Comfortable working in supply chain or related domain and managing multiple dynamic projects with Agile methodologies.