Nov 2021 – Aug 2025 · 3 yrs 9 mos · Pune, Maharashtra, India · Remote

• Successfully migrated 400+ critical Tables from Oracle to Snowflake using Qlik Replicate With CI/CD, resulting in
• improved performance and scalability.
• ● Optimized complex reports using Snowflake SQL and DBT cloud, enhancing business insights and decision-making.
• ● Imported and integrated datasets from AWS S3 into Snowflake staging using DBT and Snowflake Stage, reducing data
• processing time by 40%
• ● Orchestrated and scheduled ETL jobs with DBT scheduler, reducing manual intervention and improving efficiency by
• 80%.
• ● Conducted comprehensive testing of tables, validating data integrity and quality using qTest and Tosca ensuring data
• accuracy and consistency by 20%.
• ● Clickstream:
• Efficiently loaded real-time clickstream data from Kinesis stream into S3 using AWS Glue
• Applied Transformation to the data using AWS Glue and Pyspark, enabling accurate analysis and insights.
• Conducted thorough data checks and cleaning procedures, resulting in data accuracy improvement by 25%
• Successfully segregated and notified stakeholders of 5% bad data through SNS notifications, improving overall data
• quality
• Transformed and loaded cleansed data into Redshift tables, supporting business reporting and analysis.

Oct 2018 – Nov 2021 · 3 yrs 1 mo · Pune Area, India

• Development of Spark code using PySpark and Spark-SQL, leading to a 20% reduction in processing time.
• ● Translated business requirements into actionable insights by creating tailored reports and tables, utilizing Spark
• Context/Session, Spark-SQL, and DataFrame functionalities.
• ● Employed PySpark SQL to design and implement Partitioned Hive tables with Snappy compression, enhancing data
• storage efficiency and query performance by 30%.
• ● Discussed project progress with customers, collected feedback on different stages and directly addressed
• Concerns
• ● Resolve post-migration issues, ensuring seamless project execution and customer satisfaction.
• ● Efficiently managed and scheduled jobs through AutoSys, guaranteeing smooth and timely ETL operations.
• ● Smart DQ-
• ● Innovated a Python-based tool that significantly enhanced Reconciliation and data quality, achieving a 25% reduction in
• data inconsistencies and an overall accuracy boost.
• ● Integrated an open-source reconciliation and remediation library, automating reconciliation and remediation processes
• to reduce manual work by 70%, enhancing efficiency in data handling
• ● Employed Pandas, NumPy, and other libraries for intricate data modeling and manipulation, resulting in 30% faster
• transformation.
• ● Data Modeling, Data manipulation using Pandas and NumPy, PdfPlumber, PyPDF2, OpenCV, Tesseract, pyTesseract
• ● Crafted visualization dashboards employing Chart.js, Google Charts, and Bootstra