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Mid-level analytics engineer in Bangalore with broad toolset requirements increases candidate competition.
Analytics engineering skills transfer across industries, though ERP/procurement domain knowledge increases specificity.
Explicit 4–6 years requirement plus many mandatory tools and domain experience makes shortlisting strict.
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Design, build, and operationalize analytics-ready data products and semantic models on platforms like Databricks.
Develop and maintain data transformation pipelines using dbt, SQL, and PySpark converting raw data into trusted datasets for business use.
Partner with business teams and stakeholders to translate KPIs into reusable self-service datasets and govern dashboards in tools like Power BI and Tableau.
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 track record of delivering at least 2–3 production analytics data products end-to-end.
Experienced in bridging technical data engineering with business analytics to create business-friendly data products and dashboards.
Familiar with modern data architectures like star/snowflake schemas and medallion layering, and skilled in data governance and quality tools (Collibra, Purview, dbt tests).
Knowledgeable in analytics-driven KPI development related to spend analytics, supplier performance, demand forecast accuracy, and working capital, with awareness of AI/ML data pipeline integration.