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k kalyan bharagav

@kkalyanbharagav

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Azure Data Engineer delivering scalable ETL/ELT and lakehouse pipelines that improve efficiency and data integrity.

India

Message

What I'm looking for

I’m looking for an Azure-focused data engineering role where I can build reliable ETL/ELT and lakehouse pipelines, strengthen data quality, and help teams make faster, data-driven decisions through scalable, well-optimized systems.

I’m an Azure Data Engineer with ~4 years of experience in ETL, data migration, and data modeling. I design, optimize, and automate scalable data pipelines using SQL, Python, and Azure cloud services, with a focus on data integrity and actionable business insights.

At Capgemini, I built full and incremental load pipelines with PySpark and Azure Databricks, orchestrated workflows with Azure Data Factory, and managed structured and unstructured data in Azure Data Lake. I’ve also engineered Lakehouse processes (e.g., Bronze-layer incremental/full loads), added reusable parameterized Fabric notebooks for multi-source ingestion, and implemented data quality checks; for large-scale volumes, I’ve processed 5TB+ weekly, reducing execution time by 30% and achieving 99.9% workflow reliability.

Experience

Work history, roles, and key accomplishments

Current

Azure Data Engineer (Imperial Brands)

Current

Capgemini

Oct 2025 - Present (9 months)

Designed and developed Azure Data Factory pipelines to extract, transform, and load data into ADLS and Azure SQL Database. Built and maintained Databricks notebooks with PySpark for schema validation and data quality enforcement, and migrated legacy ETL to a Databricks lakehouse architecture.

Azure Data Factory Azure SQL Database PySpark Azure Databricks Lakehouse ETL Parameterized Modular Pipelines

Azure Data Engineer (Total Energies)

Capgemini

Feb 2023 - Sep 2025 (2 years 7 months)

Developed scalable data pipelines using PySpark and Azure Databricks for full and incremental loads, orchestrated via Azure Data Factory. Managed data in Azure Data Lake, implemented Lakehouse Bronze-layer loads, automated deployments with Azure DevOps, and improved execution time and reliability.