Sunil Bangi

• I aim to work in a challenging environment, leveraging my technical skills, knowledge, and background in consulting and analytics to design scalable data pipelines and drive data-driven decision-making. I aim to contribute positively in a challenging and collaborative environment that supports continuous learning, personal growth, as well as organizational growth.
• Data Engineer with over 4.5 years of experience designing and implementing data solutions. Proficient in Python, PySpark, SQL, AWS, Pandas. Proven track record of developing scalable data pipelines, optimizing data processing workflows, and ensuring data quality.
• Strong problem-solving and analytical skills and a deep Understanding of big data technologies and cloud platforms.
• Developed and maintained ETL pipelines using Python and PySpark, handling large-scale data processing and transformation tasks.
• Optimized SQL queries for efficient data retrieval and aggregation, resulting in a 20% reduction in query execution time.
• Designed and implemented data warehouse solutions on AWS, Redshift.
• Developed data integration workflows, enabling seamless data movement across different systems and platforms.
• Conducted data quality assessments and implemented data cleansing and validation procedures to ensure data accuracy and integrity.
• Collaborated closely with cross-functional teams to understand data requirements and provide technical solutions for data-driven initiatives.

• Data Lake Implementation and Analytics: Set up a data lake infrastructure on AWS using services like S3, Glue, and Athena, and perform advanced analytics on the stored data using Python, SQL, AWS, PySpark to Design and configure the data lake architecture, Develop ETL jobs using AWS Glue, Handle data ingestion and transformation, Create optimized data schemas in Athena, Perform complex analytics and data processing with Spark, Collaborate with stakeholders for data exploration and insights.
• Data Warehouse Optimization with PySpark and SQL: Optimize the performance of a data warehouse by implementing data partitioning, indexing, and query optimization techniques using Python, PySpark, SQL, Pandas, and Analyze and optimize the data warehouse schema, Implement partitioning and indexing strategies, Fine-tune SQL queries for performance, Optimize data loading and extraction processes, Collaborate with stakeholders to understand performance requirements.

www.linkedin.com/in/sunil-bangi