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Agasya Sandilya Devarasetty

@agasyasandilyadevara

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Data Engineer with 4+ years building scalable ETL/ELT and lakehouse pipelines on Azure and AWS.

India

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What I'm looking for

I’m looking for a role where I can build scalable ETL/ELT and lakehouse pipelines on Azure/AWS, partner cross-functionally, and own data quality and reliability with strong validation, testing, and SLA-focused production support.

I’m a Data Engineer with 4+ years designing and optimizing scalable ETL/ELT pipelines and big data processing using Medallion Architecture. I build reliable lakehouse-style data solutions with strong expertise in Python, SQL, Apache Spark, Hadoop, and Databricks, with hands-on experience on cloud based data platforms (Azure & AWS).

At PwC, I designed and implemented data pipelines processing 10TB of raw data into a data lake, improved execution efficiency, and reduced compute cost by 15% by migrating legacy Hive scripts to optimized Spark jobs. I also focus on production-grade quality—diagnosing issues, ensuring SLA adherence, and maintaining 99.9% data accuracy through end-to-end data validation, unit testing, and root-cause analysis, including financial data and analytics use cases.

Experience

Work history, roles, and key accomplishments

Data Engineer

PwC

Jun 2022 - Dec 2023 (1 year 6 months)

Designed and implemented scalable ETL pipelines on Azure Databricks and Azure Data Factory, transforming 10TB of raw data into a data lake using Medallion Architecture. Reduced compute cost by 15% by migrating legacy Hive scripts to optimized Spark jobs and maintained 99.9% data accuracy through end-to-end validation, unit testing, and root-cause analysis.

Apache Spark Databricks Azure Data Factory Delta Lake Medallion Architecture Delta Table Design Data Validation Unit Testing

Big Data Engineer

Atos-Syntel

Feb 2020 - May 2022 (2 years 3 months)

Built Hive data models and HQL queries to transform raw data into analytics-ready datasets for the Global Wealth Assets & Investments domain (Manulife project). Implemented hierarchical staging with SCD Type 1/2 for historical reporting, unit-tested queries, and delivered scalable transformations across distributed clusters using DataFrames and Spark SQL.

Apache Hive Apache Spark DataFrames SCD Type 1 Data Modeling Unit Testing Distributed Data Processing