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Tier-1 brand, mid-level generalist data role, metro location, and broad skillset requirements increase competition.
Core data engineering skills transfer across industries, but ERP/procurement domain needs increase specificity.
Explicit 4–6 years plus mandatory dbt/Databricks, ERP and production analytics product requirements create strict filters.
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Design, build, and operationalize analytics-ready data products and semantic models using modern platforms such as Databricks with star/snowflake schemas and medallion architecture.
Develop and maintain transformation pipelines using dbt, SQL, and PySpark, and build/optimize enterprise dashboards in Power BI, Tableau, supporting KPIs across procurement, supply chain, finance, and operations.
Ingest and manage ELT pipelines from SAP S/4HANA, Oracle, Salesforce, Workday, while championing data governance, lineage, cataloging, and implementing data quality and observability checks.
4–6 years of professional experience in analytics engineering, data engineering, or BI development roles.
Bachelor's or Master's degree in Computer Science, Data Science, Information Systems, Engineering, Statistics, or related field.
Experience with enterprise ERP data (SAP S/4HANA, Oracle) and domain knowledge in procurement, supply chain, or finance analytics.
Proven delivery of at least 2–3 production analytics data products end-to-end.
Strong technical proficiency bridging data engineering and business analytics with hands-on expertise in building reusable, self-service datasets and enterprise dashboards.
Comfortable working with complex ERP data and translating KPIs such as spend analytics and demand planning into performant data solutions.
Experience in data governance, quality, and observability tools and understanding of AI/ML fundamentals related to feeding analytics datasets into ML pipelines.