Keerthana Nibhanapuri

Enterprise Cloud Data Platform Modernization

• Designed and developed scalable ETL pipelines using Apache Airflow (MWAA) for workflow orchestration.
• Built PySpark transformation jobs using AWS Glue to process large-scale datasets stored in Amazon S3.
• Developed AWS Lambda functions to automate file movement and trigger Glue jobs using S3 events.
• Configured AWS AppFlow to integrate Salesforce and SAP data into Amazon S3.
• Implemented SodaCL-based data quality validations covering schema, null, duplicate, primary key, uniqueness, volume, and referential integrity checks.
• Leveraged Glue Catalog and Crawlers for metadata management and automated schema discovery.
• Optimized Glue jobs using partitioning and compression techniques, improving performance and reducing storage costs.
• Performed root cause analysis and performance tuning, improving pipeline efficiency by 25%.
• Automated end-to-end ingestion, transformation, and reporting workflows, reducing manual effort by 40%.
• Maintained GitHub repositories, supported CI/CD deployments, and prepared technical documentation.

• Developed Snowflake SQL scripts to create Permanent, Transient, Temporary, External, and Work tables based on business requirements.
• Built SQL transformations and data extracts from Snowflake tables to support insurance dashboards and business metrics.
• Performed data validation between Teradata and Snowflake by validating record counts, schema, data types, aggregates, and business rules to ensure migration accuracy.
• Assisted in migrating Teradata workloads to Snowflake by implementing ELT logic, optimizing SQL queries, and supporting testing activities.
• Collaborated with business analysts and reporting teams to validate data quality and deliver accurate reporting datasets.
• Utilized GitHub, GitHub Copilot, Windsurf IDE, and Snowflake Cortex AI to improve SQL development, debugging, code optimization, and productivity.

Enterprise Banking Data Platform

• Designed ETL workflows using Oracle Data Integrator (ODI 12c) based on business requirements.
• Developed PySpark applications for distributed data processing and transformation.
• Created Hive external tables with partitioning, dynamic partitioning, and bucketing.
• Processed CSV and Parquet files using Spark SQL and loaded curated datasets into Hive.
• Developed PySpark scripts for Slowly Changing Dimensions (SCD), incremental loads, and Oracle-to-Hive transformations.
• Built Unix wrapper scripts for automated data loading, merging, and secure SFTP transfers.
• Performed SQL optimization, debugging, unit testing, and managed database objects including tables, views, stored procedures, and triggers.

AWS Data Engineering Platform

• Developed scalable data pipelines for processing enterprise-scale datasets.
• Designed Apache Airflow DAGs to automate ETL workflows.
• Built Spark SQL transformations on data stored in Amazon S3.
• Worked extensively with AWS services including EC2, EMR, Athena, MWAA, CloudWatch, RDS, and S3.
• Developed Python scripts for automated data processing and loading into Amazon S3.
• Collaborated with business teams to translate requirements into scalable data engineering solutions.
• Supported production monitoring, troubleshooting, and issue resolution.

• Completed intensive training in Python, DBMS, Java, and Microsoft Business Intelligence (MSBI).
• Developed enterprise reports using SSRS.
• Integrated data from multiple sources using SSIS.
• Created OLAP cubes using SSAS.
• Recognized as a High Performer during training.