Sep 2022 – May 2026 · 3 yrs 8 mos · India

• Designed, developed, and maintained scalable data pipelines using Airflow, Databricks and Azure services to support analytics workloads.
• Managed and optimized the Azure cloud environment to ensure seamless scalability, high availability and cost efficiency.
• Identified high-cost components and implemented targeted optimizations, reducing overall cloud expenses by 30%+ .
• Built and maintained CI/CD pipelines u s i n g Azure DevOps, enabling automated deployments and faster delivery cycles.
• Implemented performance tuning strategies on Databricks workflows, including efficient query design, optimized joins, partitioning, and predicate pushdown, achieving ~40% faster processing, reduced runtimes and lower infrastructure costs.
• Performed in-depth debugging, code reviews and feature development, ensuring high code quality and long-term maintainability.

Apr 2022 – Sep 2022 · 5 mos · Bengaluru, Karnataka, India

• Collaborated directly with clients to gather requirements and translate business needs into scalable data solutions.
• Developed TDD-based ingestion frameworks for SAP, manual files, and third-party sources, harmonizing data into BigQuery.
• Wrote complex SQL transformations — including multi-level joins, hierarchy flattening, calendar conversions, pivot/unpivot — to build standardized product-layer views for BI usage.
• Integrated data from TPM systems, applying business rules, performing data wrangling, cleansing, transformations, and reconciliation. Built workflows to write back processed data to SAP and external systems , ensuring end-to-end data consistency.

Oct 2021 – Apr 2022 · 6 mos

• VODAFONE NGBI: The objective of this project is to migrate the data for Vodafone - Portugal On-Premise system and create a model for easy analysis of their service areas. The activity of this project is to Extract data from their on-premise machines, Transform and Load data into Big Query using Google Data Fusion.
• Gathered the requirements & understood the source data model to implement the business requirements.
• Transformed On-Premise historical raw data using SQL scripts and loaded to BigQuery tables.
• Designed and Developed data pipelines in Google Data Fusion to migrate batch raw data from Cloud Storage to intermediate files by transforming and loading encrypted data using widgets like BigQuery Executor, Joiners and GCS wrangler etc...
• The intermediate data is loaded into BigQuery Fact and Dimension tables where it manages both current and historical data over time using loading types such as insert, upsert and SCD.
• Deployed and Executed Data Fusion pipelines on Spark cluster using DataProc service.
• Scheduled and monitored Data Fusion pipelines using Airflow Composer.
• Solved, analyzed, and suggested solutions on issues identified from Production, UAT and lower environments.
• Involved in optimizing the performance of SQL Queries and ETL Pipelines.
• Developed automated Python scripts to deploy tables in BigQuery and to generate dummy data for Unit Testing.

Oct 2019 – Sep 2021 · 1 yr 11 mos