Bhargav Sai Meda
Data Engineer with 4.11 years of experience in the IT industry at TCS, with strong expertise in SQL and hands-on experience in PySpark and GCP-based data processing. Proficient in writing complex SQL queries including joins, aggregations, subqueries, and window functions. Hands-on experience in PySpark for building ETL pipelines, performing data cleaning, transformations, and joins. Strong understanding of distributed processing, Spark internals, and performance optimization techniques.
Experience in handling large datasets and improving performance using partitioning, caching, and broadcast joins. Knowledge of file formats such as Parquet, CSV, and JSON with focus on efficient storage and querying. Familiar with GCP services including GCS, Big Query, and Dataproc for building scalable data pipelines. Strong problem-solving skills with ability to debug and optimize data workflows.
Experience
Data Engineer
TCS
Professional Summary: Data Engineer with 4.11 years of experience in the IT industry at TCS, with strong expertise in SQL and hands-on experience in PySpark and GCP-based data processing.
- Proficient in writing complex SQL queries including joins, aggregations, subqueries, and window functions.
- Hands-on experience in PySpark for building ETL pipelines, performing data cleaning, transformations, and joins.
- Strong understanding of distributed processing, Spark internals, and performance optimization techniques.
- Experience in handling large datasets and improving performance using partitioning, caching, and broadcast joins.
- Knowledge of file formats such as Parquet, CSV, and JSON with focus on efficient storage and querying.
- Familiar with GCP services including GCS, Big Query, and Dataproc for building scalable data pipelines.
- Strong problem-solving skills with ability to debug and optimize data workflows.
Education
St.Ann's college of Engineering and Technology
B.Tech
Electronics and Communication Engineering
Skills
SQLPythonPySparkGoogle Cloud Platform (GCP)GCSBigQueryDataprocIBM DataStageCognosVS CodeGitCSVJSONParquet
Project #1: (Previously Worked)
Enterprise Data Integration & ETL System
DataStage Developer
Worked on developing and maintaining enterprise ETL solutions for the insurance domain using IBM DataStage. The project involved extracting, transforming, and loading large volumes of data from multiple source systems into data warehouses and reporting platforms to support business operations and analytics. Responsibilities
- Designed, developed and maintained ETL jobs using IBM DataStage for data integration and transformation requirements.
- Created and managed parallel and server jobs, sequences, shared containers, and reusable components
- Optimised DataStage jobs for improved performance, scalability and reduced execution time.
- Troubleshot and resolved job failures, performance bottlenecks and data related issues. Achievements
- Improved DataStage jobs performance by 30% through job optimisation, partitioning, and parallel processing techniques.
- Resolved production issues within SLA timelines, ensuring uninterrupted business operations.
Project #2: (Previously Worked)
Data Processing & Analytics Pipeline
Data Engineer
Designed and implemented an end-to-end ETL pipeline using PySpark and GCP to process large-scale insurance data. The solution enabled efficient data transformation, storage, and analytics for business insights. Responsibilities
- Developed ETL pipelines using PySpark to read and process data from GCS.
- Performed data cleaning, transformation, and enrichment using joins and window functions.
- Applied aggregations and business logic for analytics-ready datasets.
- Optimized pipelines using broadcast joins, caching, and partitioning techniques.
- Stored processed data in Parquet format to improve performance and reduce storage costs.
- Loaded transformed data into BigQuery for reporting and analysis. Achievements
- Improved pipeline performance by 40% using optimization techniques.
- Built scalable ETL pipelines for handling large datasets efficiently.
- Reduced query execution time using partitioning and optimized transformations.