Data Engineer with 3+ years of production experience — currently working at the intersection of enterprise Azure data engineering and utility-scale analytics at TPWODL.

As a Lead Engineer at TP Western Odisha Distribution Limited (a joint venture of Tata Power and the Government of Odisha), I work on enterprise-grade data and analytics solutions that support power distribution operations across multiple Tata Power discom entities. My primary focus right now is SAP BW to Azure data modernization — migrating legacy SAP reporting infrastructure onto a modern Azure stack using ADF, Databricks, and Spark, while building Power BI solutions on top for business reporting.

Before TPWODL, I spent two years at Infosys as part of a production Snowflake data engineering team for Mercedes-Benz USA & Canada — building Bronze-Silver-Gold ELT pipelines, automating ingestion with Snowpipe, and implementing CDC using Streams and Tasks. I also worked independently at Troy Consultancy owning the internal BI function end-to-end with Power BI and SQL Server.

I hold a Snowflake Data Engineering Professional Certificate, a Microsoft Azure Essentials Professional Certificate, and a Microsoft Career Essentials in Generative AI certificate.

Lead Engineer, IT (Data Engineering & Analytics) Jun 2026 – Present | Bhubaneswar, Odisha

TPWODL is a joint venture between the Government of Odisha and Tata Power, responsible for electricity distribution across Western Odisha. The IT data team supports analytics and reporting across all Tata Power distribution companies — CODL, WODL, NODL, SODL, DDL (Delhi), and MDL (Mumbai).

• Working on SAP BW to Azure data modernization — migrating legacy SAP BW-based reporting infrastructure to a modern Azure data platform using Azure Data Factory, Azure Databricks, and Apache Spark
• Building and maintaining enterprise data integration and transformation pipelines connecting Oracle SQL, IBM Db2, and SAP BW source systems into the Azure ecosystem
• Developing Power BI dashboards and enterprise reports for business operations, analytics, and regulatory reporting across Tata Power discom entities
• Collaborating with cross-functional business and IT teams to map data flows, gather reporting requirements, and translate them into scalable data solutions
• Supporting data validation, quality checks, and reconciliation across source and target systems to ensure data integrity throughout the modernization process
• Contributing to analytics solutions across multiple distribution companies under the Tata Power umbrella, with MDL (Mumbai) as a key reporting responsibility area

Stack: Azure Data Factory · Azure Databricks · Apache Spark · Power BI · Oracle SQL · IBM Db2 · SAP BW · PySpark · Spark SQL

Consultant (Internal BI & Data) Jul 2024 – Jun 2025

• Designed and developed Power BI dashboards from scratch based on business requirements, improving reporting efficiency by ~30% and enabling faster decision-making across departments
• Built data integration workflows connecting Excel, SQL Server, and web sources into Power BI for real-time operational reporting
• Managed and maintained the employee database in SQL Server, developing queries and stored procedures for HR analytics and performance tracking
• Optimized existing reports and queries for performance, achieving up to 25% faster load times and reducing dashboard refresh cycles

Stack: Power BI · SQL Server · DAX · Power Query · Excel

Systems Engineer (Client: Mercedes-Benz USA & Canada) May 2021 – Jan 2023

• Part of a production Snowflake Data Engineering team supporting Mercedes-Benz USA & Canada's analytical data platform, working across the full SDLC — from requirement analysis and dimensional modeling through pipeline development, testing, and monitoring
• Used Azure Data Factory to orchestrate batch pipelines processing customer transactions (credit, debit, vehicle financing) across multiple dealership showrooms into Snowflake
• Ingested and processed structured and semi-structured data (CSV, JSON, Parquet) from ADLS and AWS S3 into multi-layered Snowflake tables (Bronze → Silver → Gold)
• Implemented Snowpipe for automated ingestion of customer interaction logs, profile updates, and vehicle service records — reducing manual processing by ~40%
• Built Streams and Tasks for incremental data loads and CDC automation across multi-layered tables, cutting manual refresh work by ~50%
• Monitored and optimized warehouse performance and query execution using Snowflake Account Usage views, achieving ~20% reduction in compute costs
• Applied data validation and quality checks at ingestion and transformation stages to ensure consistency and reliability of analytical datasets
• Used Time Travel for debugging, data recovery, and reviewing historical changes during incidents

Stack: Snowflake · Azure Data Factory · Snowpipe · Streams & Tasks · CDC · ADLS · AWS S3 · SQL · Python

End-to-end Snowflake data platform with AI-assisted development and natural language querying.

Built a Medallion Architecture (Bronze → Silver → Gold) pipeline processing 13+ source files into an analytical Star Schema with SCD Type 2 historical tracking across 80K+ records. Used Cortex Code for AI-assisted SQL and pipeline development, and built a Cortex Analyst semantic model (YAML) enabling business users to query 86K+ sales records in plain English — no SQL required.

Enterprise-style data warehouse on SQL Server with full Bronze → Silver → Gold implementation.

Integrated CRM and ERP source data, built stored procedure-based ETL logic, modeled a star schema Sales Data Mart with dim_customers, dim_products, and fact_sales, and implemented full data quality checks across layers.

Production-style Snowflake pipeline modeled on a food delivery platform.

Covers initial and delta loads, CDC using Streams, SCD Type 2 dimensions, a star schema fact table at order-item granularity, data governance with Tags and Masking Policies, and full automation via Stored Procedures and Tasks.

Enterprise-scale retail analytics for a 5M+ customer ecommerce company across 15 countries.

Built on Snowflake with ADLS as external stage, ingesting CSV, JSON, and Parquet. Implements Bronze → Silver → Gold layers, CDC with Streams, data quality pipelines, and Gold layer views for sales performance, customer segmentation, and product analytics.

"Turning raw data into decisions — one pipeline at a time."