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Punit SoniPS
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Punit Soni

@punitsoni

Data engineer specializing in cloud-native ETL/ELT, Azure Databricks, Snowflake, and near-real-time Python/Spark pipelines.

India
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What I'm looking for

I seek a mid-level cloud data engineering role building reliable, scalable ETL/ELT and streaming pipelines, with strong emphasis on data quality, governance, and cross-functional collaboration.

I am a Data Engineer with four years of experience building and scaling cloud-native data platforms on Azure, with additional exposure to AWS. I specialize in Snowflake-based data warehousing, ETL/ELT pipelines, and near-real-time processing using Python and Apache Spark.

I have engineered end-to-end pipelines using Azure Data Factory, Databricks (PySpark), and Snowflake to process tens of millions of records for large enterprise workloads, applying Medallion Delta Lake architectures and optimized Spark transformations.

My work includes CDC-based ingestion, advanced SQL query optimization, Python-based validation frameworks, and designing Lakehouse and ETL solutions on ADLS Gen2, Synapse, and Snowflake. I focus on pipeline reliability, data quality, and governance aligned with compliance requirements.

I collaborate with architects and stakeholders to define schema validation, automated exception handling, lineage, and audit logging. I hold a B.Tech. in Computer Science and certifications, including Microsoft Certified: Azure Data Engineer Associate.

Experience

Work history, roles, and key accomplishments

RG
Current

Data Engineer

RWS Group

Jul 2022 - Present (3 years 8 months)

Built and scaled cloud-native ETL/ELT pipelines on Azure and Snowflake processing 15M–70M+ records per pipeline, improving ingestion performance (20–30%) and reducing manual validation effort by 60% through automated validation and governance frameworks.

Education

Degrees, certifications, and relevant coursework

MU

Medi-Caps University

Bachelor of Technology, Computer Science Engineering

2018 - 2022

Grade: 8.64 / 10

Completed a Bachelor of Technology in Computer Science Engineering with a focus on software and data engineering fundamentals, graduating with a CGPA of 8.64/10.

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