Summary
Overview
Work History
Education
Skills
Certification
Project Highlight
Timeline
Generic

Girvani A

Summary

Experienced Azure Data Engineer with 4 years in designing and delivering scalable data solutions in the Azure ecosystem. Specialized in Azure SQL, Azure Data Factory, Synapse Analytics, Databricks. Currently working on dealer planning analytics for Mercedes-Benz, delivering data-driven insights that enhance decision-making.

Overview

4
4
years of professional experience
1
1
Certification

Work History

Azure Data Engineer

Publicis Sapient
05.2021 - Current
  • Develop data pipelines using Azure Data Factory (ADF) or Azure Synapse Pipelines.
  • Ingest data from multiple sources (on-prem, cloud, APIs, files) into Azure Data Lake or SQL databases.
  • Implement incremental loads, CDC (Change Data Capture), or ETL/ELT processes.
  • Use Azure Databricks (PySpark/Spark SQL) or Synapse Spark Pools for large-scale data transformations.
  • Design and implement metadata-driven data pipelines to enable configurable, reusable, and scalable workflows.
  • Develop and manage control tables or configuration files (in SQL, JSON, or ADLS) to drive pipeline behavior dynamically.
  • Improve pipeline maintainability and reduce duplication through metadata abstraction.
  • Monitor and improve performance of pipelines and queries using query plans, index tuning, and resource monitoring.
  • Use Azure Key Vault for secret management.
  • Use CI/CD pipelines with Azure DevOps or GitHub for deploying data pipelines and notebooks.

Education

Master of Computer Applications -

VTU
Bengaluru, India

Skills

  • Azure Data Factory
  • Azure SQL Database
  • Azure synapse Analytics
  • Databricks
  • Fabric
  • PySpark, Spark SQL
  • Power BI

Certification

  • Microsoft Certified: Azure Data Engineer Associate

Project Highlight

Key Responsibilities

Dealer Planning Analytics – Mercedes-Benz, developed and maintained a data engineering solution to support strategic dealer planning and performance analysis across global markets. The solution provided actionable insights to the business team on dealer coverage, performance KPIs, sales forecasts, and market potential by building a scalable Azure-based data platform.


  • Designed and implemented data pipelines using Azure Data Factory to ingest and transform data from multiple sources including SAP, flat files, APIs, and databases.
  • Built metadata-driven ETL pipelines to handle dynamic loading and transformation logic across dealer, sales, and financial datasets.
  • Developed PySpark-based transformations to process and clean large volumes of dealer performance data, sales trends, and capacity metrics.
  • Integrated data into Azure Data Lake Gen2 and curated views in Azure Synapse Analytics for reporting and advanced analytics.
  • Modeled data in star and snowflake schemas for dealer network and market analysis.
  • Created partitioning, indexing, and distribution strategies to optimize query performance on large datasets (~100M+ records).
  • Enabled self-service BI by exposing curated data to Power BI via Synapse, supporting KPIs such as dealer profitability, sales vs. forecast, coverage gap, and service capacity.
  • Implemented data governance by integrating Azure Purview for metadata and lineage tracking.
  • Secured pipelines using Azure Key Vault, managed identities, and role-based access control (RBAC).
  • Deployed solutions using Azure DevOps CI/CD pipelines, enabling version-controlled, automated deployments across environments.

Timeline

Azure Data Engineer

Publicis Sapient
05.2021 - Current

Master of Computer Applications -

VTU

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Girvani A