Bharathi Govindarajan

• UK based Company • Designed retail and CPG data harmonization solutions using Azure Databricks and Azure Data Lake, enabling the processing of millions of records per hour for analytics and reporting. • Defined scalable data architecture patterns to integrate and standardize high-volume hourly retailer feeds from multiple data sources into a unified analytical platform. • Architected modern Databricks Lakehouse solutions capable of supporting large-scale hourly data processing, KPI reporting, and business intelligence workloads. • Implemented data quality, governance, and harmonization frameworks to ensure accuracy and consistency across continuously refreshed hourly retail datasets. • Designed enterprise data platforms supporting high-frequency hourly data ingestion, transformation, and analytics for retail and CPG clients, enabling timely business insights and decision-making.

• PROJECT: FRANCE RETAIL MARKET RESEARCH & CONSUMER INSIGHTS PLATFORM: • Lead data engineering for France retail insight platforms, turning product, retailer and consumer behaviour data into analytics-ready datasets for major retail stakeholders. • Designed scalable reconciliation and transformation pipelines in Databricks/PySpark to support transaction-level validation, retailer mapping and data quality checks. • Built curated datasets for Power BI, KPI reporting and product intelligence, enabling faster analysis of retailer performance, consumer trends and FMCG market behaviour. • Work with France business teams, architects and global stakeholders to shape cloud-aligned analytics architecture and repeatable data quality frameworks.

• Delivered enterprise-scale Databricks and PySpark pipelines for CPG analytics use cases across global brands including PepsiCo and Coca-Cola. • Implemented Delta Lake architecture with partitioning, caching, Z-Ordering and Spark tuning to improve pipeline reliability and query performance. • Built ETL/ELT frameworks for sales, supply chain, inventory and retail analytics, producing trusted analytical datasets for BI and business users. • Managed agile deliverables, reviewed engineering quality and mentored junior engineers while supporting cloud modernisation and CI/CD-enabled releases.

• Developed Azure-based data platforms using ADF, Azure Databricks, ADLS Gen2, Azure SQL and PySpark for enterprise analytics and reporting workloads. • Designed metadata-driven ADF orchestration and incremental processing frameworks to reduce manual intervention and support repeatable ingestion patterns. • Built distributed transformation workflows for structured and semi-structured data, strengthening cloud migration and data modernisation programmes. • Optimised processing logic and storage patterns to improve

• Started in enterprise ETL, SQL and data transformation, supporting client data integration, validation and reporting activities. • Enhanced and maintained data workflows, building a strong foundation in pipeline engineering, data quality and production support.