Tarun Kumar Email & Phone Number

Houston, Texas, United States

• Built and maintained ETL workflows with AWS Glue for data ingestion from multiple sources (APIs, flat files, NoSQL and RDBMS) into Snowflake, Hive, and Amazon Redshift, using Python and SQL improving efficiency by 35%.
• Utilized AWS EC2 and EMR for PySpark data processing, optimizing computation and reducing processing time by 40%.
• Designed data models in Snowflake with dbt for standardized schemas, boosting query performance and Matillion for ETL operation which increase data accessibility by 20%.
• Orchestrated complex workflows using Airflow and AWS Step Functions using Python and SQL as language to automate data processing, enhancing efficiency and reducing manual interventions.
• Developed data transformations in Databricks with PySpark and Python, using Pandas, SQLAlchemy and scikit-learn for prescriptive analytics.
• Integrated real-time data from Kinesis, Kafka and DynamoDB into Snowflake, enabling continuous analytics and improving efficiency by 30%.

Boston, Massachusetts, United States

Hyderabad, Telangana, India

• Leveraged PySpark and Airflow frameworks to design and deploy ETL data pipelines, seamlessly integrating and processing 2+ TB of clinical trials data from 5 distinct sources (csv files, APIs, MySQL tables) enabling.
• Initiated API calls through postman to fetch all the responses in Json and load the Json’s to staging layer in S3.
• Built scripts that perform end to end data cleansing process using SQL for 2 million records per migration.
• Analyzed ETL process failures, data anomalies, and data warehousing issues identified by 300+ end-users.
• Corrected the mapping of therapeutic area to disease for 5 data sources, resulting in 100% accuracy of mapping using PySpark SQL.

India

• Managed 50+ data analysis related requests from clients, traced issues, and proposed efficient resolution within 24 hours.
• Created 25+ data testing scenarios in the application to ensure claims data accuracy.
• Gathered detailed requirements from clients and compiled comprehensive documentation of functional and technical requirements, fostering project clarity.
• Implemented Spark SQL Scripts to effectively store monthly incremental claims data in S3, resulting in a 30% reduction in data storage errors and improved data integrity.
• Reduced average search time to 30 MS by working on query optimization techniques using advanced SQL aggregation functions.
• Formulated recommendations and support to senior team members for the project implementation plans

Master'S Degree, Data Analytics, 4.0

Bachelor Of Technology - Btech, Mechanical Engineering

Executive Pg, Data Science