We are seeking a hands-on Technical Data Analytics Engineer to design, build, and operationalize analytics-ready data products, semantic models, and self-service BI solutions that enable data-driven decision-making across the enterprise. The ideal candidate bridges the gap between data engineering and business analytics transforming raw data into curated, trusted, and performant datasets that power dashboards, KPIs, and advanced analytics use cases. This role partners closely with business stakeholders, data engineers, BI developers, and functional leaders across procurement, supply chain, finance, and operations.

- Design and build curated, analytics-ready data models (star/snowflake schemas, medallion architecture bronze/silver/gold layers) on modern platforms like Databricks,
- Develop and maintain transformation pipelines using dbt (data build tool) , SQL, and PySpark to convert raw enterprise data into trusted, business-friendly datasets.

- Partner with business teams to translate KPIs (e.g., spend analytics, supplier performance, demand forecast accuracy, working capital) into reusable, self-service datasets .

- Build, optimize, and govern enterprise dashboards and reports in Power BI, Tableau etc

- Design and maintain ELT pipelines to ingest data from SAP S/4HANA, Oracle, Salesforce, Workday,



and third-party APIs into the enterprise data lake/warehouse.

- Champion data lineage, cataloging, and governance using tools like Collibra, Purview, Alation, or Unity Catalog

- Implement data quality checks, tests, and observability using dbt tests, Outstanding Expectations, Monte Carlo Qualifications

- 4 6 years of professional experience in analytics engineering, data engineering, or BI development roles.
- Proven track record of delivering at least 2 3 production analytics data products end-to-end.
- Bachelors or Masters in Computer Science, Data Science, Information Systems, Engineering, Statistics, or a related field.

- Experience with enterprise ERP data (SAP S/4HANA, Oracle) and modeling for procurement, supply chain, or finance domains.
- Understanding of AI/ML fundamentals and how analytics datasets feed ML pipelines.

- Exposure to spend analytics, supplier scorecards, demand planning, or working capital analytics .

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.

📌 Data and Analytics Engineer (Bengaluru)
🏢 Western Digital
📍 Bengaluru