Akshay Nalawade

Senior Data Engineer

Cognizant•  March 2022 - March 2023

Led the optimization of data processing jobs using PySpark, resulting in a 40% reduction in data processing times for datasets over 5TB. Refined Spark jobs to handle large-scale data processing, improving performance and reducing resource consumption by 25%. Adopted Parquet as the default storage format, decreasing storage costs by 22% and also speeding up data retrieval time. Designed and constructed scalable data pipelines using Apache Kafka, handling real-time data with 99.9% uptime, improving overall data processing efficiency. Spearheaded the design and implementation of Big Data solutions, improving company's data processing capabilities by 40%.

Lead Data Engineer

SS&C technologies•  July 2020 - March 2022

Leveraged Apache Spark to integrate and transform diverse data sources, achieving 99.9% data accuracy and a 20% improvement in query response times. Streamlined data extraction and transformation processes using SQL and PySpark, lowering query run time by 40%. Transformed data storage practices by consolidating Amazon S3 buckets, enhancing organization and retrieval efficiency. Led the design and development of distributed data processing pipelines on AWS, securing a 40% improvement in data accessibility and processing efficiency.

Business Intelligence Engineer

Accenture•  October 2019 - July 2020

Integrated advanced SQL techniques, including subqueries and window functions, to identify over 500 duplicate records and rectify missing values and outliers. Reduced data pipeline failures by 35% through continuous monitoring, issue resolution, and implementation of robust CI/CD practices using Airflow and Docker. Enhanced ETL processes using PySpark accomplishing a 50% boost in data processing efficiency across varied environments. Adopted Agile methodologies to streamline project delivery, resulting in a 20% increase in project delivery efficiency and fostering collaboration among cross-functional teams.

Data Analyst

Hexaware technologies•  January 2015 - September 2019

Analyzed customer data using Power BI along with use of DAX functions to uncover purchasing patterns, resulting in a $300K increase in average order value. Prepared optimized SQL queries to cut the query processing time by 30% enhancing data retrieval efficiency. Utilized Excel's VLOOKUP’s and Pivot charts to generate insightful visualizations of data, applying techniques such as slicers, calculated fields, enhancing data interpretation efficiency by 40%.