Santosh Jena

• Company Overview: Client: Genentech
• Developed and optimized AWS Glue-based ETL pipelines for processing large-scale pharmaceutical data, ensuring high performance and reliability.
• Manipulated and analyzed large or complex data sets using relevant techniques.
• Utilized AWS SageMaker for building and deploying machine learning models to support drug research, clinical trials, and patient analytics.
• Designed and implemented Athena-based query solutions for real-time data analysis.
• Automated data ingestion, transformation, and validation using Python and PySpark, enhancing efficiency and data accuracy.
• Performed data validation and reconciliation to ensure the integrity and consistency of migrated data within AWS and on-premise environments.
• Collaborated with cross-functional teams including data scientists, engineers, and business stakeholders to drive insights and improve operational efficiency.
• Analyzed ETL processes to extract, transform, and load pharmaceutical data into AWS Redshift, S3

• Company Overview: Client: DICKS SPORTING GOODS
• Analyzed sales data to identify opportunities for revenue growth and operational efficiency improvements.
• Collaborated with cross-functional teams including data engineers, architects, and business stakeholders to ensure successful migration outcomes.
• Designed and implemented data pipelines to extract, transform, and load data into Snowflake using Snowflake utilities and ETL tools.
• Automated data extraction and reporting processes using Python scripts, reducing manual effort and improving efficiency.
• Performing data validation and reconciliation to verify the integrity of migrated data and ensure consistency with source systems.
• Analyzing ETL processes to extract, transform, and load data into Snowflake as part of Modernization.

• Company Overview: Client: Apple
• Partnered with cross-functional teams and business stakeholders to design and implement scalable data pipelines using AWS Glue and PySpark, enhancing data integration and processing efficiency .

• Designed and deployed end-to-end ETL workflows leveraging AWS Glue, S3, and Athena to support real-time and batch data processing, improving data availability for analytics and reporting.

• Developed reusable ETL scripts and automation logic to reduce manual intervention, streamline data ingestion processes, and ensure consistent data quality.

• Led optimization initiatives for AWS Glue jobs and Spark performance tuning, resulting in a 40% reduction in ETL processing time and improved job reliability.

• Collaborated with data analysts and data scientists to deliver curated datasets, enabling advanced analytics and machine learning model development.

• Implemented data quality checks and monitoring frameworks to proactively identify anomalies and bottlenecks in data pipelines.

• Delivered performance dashboards and operational KPIs using tools like QuickSight and Redshift, enabling data-driven decision-making for senior leadership.

• Conducted root cause analysis for data pipeline failures, driving the resolution of recurring issues and implementing long-term improvements in data architecture.

• Company Overview: Client: JP MORGAN
• Designed and developed ETL processes and data pipelines to ingest, transform, and load data from various sources into data warehouses and data lakes.
• Implemented real-time data streaming solutions using Apache Kafka and Spark Streaming for processing and analyzing streaming data.
• Conducted performance tuning and optimization of data processing workflows to improve efficiency and reduce processing times.
• Collaborated with data architects and database administrators to design and implement data models and schemas for optimal performance and scalability.
• Provided technical guidance and support to junior data engineers, ensuring adherence to best practices and standards.
• Implemented database migrations and data modeling using Django ORM to ensure data integrity and consistency.
• Client: JP MORGAN

• Company Overview: Client: TIVO
• Led the design and development of automated test frameworks using Python.
• Implemented best practices for test automation, including framework architecture, coding standards, and documentation.
• Collaborated with cross-functional teams to define test strategies, identify automation opportunities, and prioritize test coverage.
• Developed and executed automated test scripts for functional, regression, and integration testing.
• Integrated automated tests into CI/CD pipelines for continuous testing and deployment.
• Mentored junior automation engineers and provided technical guidance and support.
• Client: TIVO

• Company Overview: Client: IBM
• Scripted test cases in Python, Shell, and Perl to automate system configuration, deployment, and validation tasks.
• Collaborated with AIX system administrators and developers to identify test scenarios, prioritize test coverage, and resolve issues.
• Integrated automated tests into CI/CD pipelines for continuous testing and deployment of AIX-based applications.
• Conducted root cause analysis and provided recommendations for improving AIX system reliability, security, and performance.
• Stress testing and performance profiling to assess the scalability and responsiveness of AIX file systems under heavy load conditions.
• Client: IBM