Ashok MacHa

About Ashok MacHa

Highly skilled Data Engineer with 10+ years of hands-on experience in designing, developing, and optimizing data pipelines, architectures, and large-scale data solutions. Proficient in leveraging tools such as Apache Spark, Hadoop, and Kafka to process and manage high-volume data workflows. Adept at collaborating with cross-functional teams to deliver data-driven insights and scalable solutions that support business objectives. Expertise includes ETL development, cloud-based data platforms (AWS, Azure, GCP), and database management (SQL, NoSQL). Recognized for driving efficiency, improving data accuracy, and implementing best practices in data engineering, ensuring reliable and actionable analytics. Passionate about utilizing cutting-edge technologies to solve complex data challenges and deliver impactful results.\"

Experience

1. Data Engineer

   The Home Depot

   Feb 2023 — Present · Atlanta, GA, US

   Responsible for design, development and delivery of data from Operational systems and files into ODS, downstream Data Marts and Files. • Developed Python scripts to find the vulnerabilities with SQL Queries by doing SQL injection, permission checks and analysis. Worked on complex SQL Queries, PL/SQL procedures and covert them to ETL tasks. • Used Cloud watch for monitoring the server’s (AWS EC2 Instances) CPU utilization and system memory. • Involved in running Hadoop streaming jobs to process Terabytes of data. • Integrated services like Bitbucket AWS Code Pipeline and AWS Elastic Beanstalk to create a deployment pipeline. • Loaded the tables from the azure data lake to azure blob storage for pushing them to Snowflake.• Built the code efficiently and worked with business analyst, end users and architects. • Developed and deployed stacks using AWS Cloud Formation Templates (CFT) and AWS Terraform. • Involved in scheduling Oozie workflow engine to run multiple Hive jobs. • Developed new ETL process in Spark and converted the existing Hive scripts into Spark. • Implemented custom UDF using Spark RDDs, Data frames, and Spark SQL. • Implemented Azure data pipelines to migrate data from different sources to Azure Data Lake using ADF. • Designed the optimal performance strategy and managed the technical metadata across all ETL jobs. • Implemented scripts in PySpark to validate the data and automated the scripts. • Written PySpark job in AWS Glue to merge data from multiple tables and utilized AWS Glue to run ETL jobs and run aggregation on Pyspark code.• Involved on designing, developing, testing, tuning and building a large-scale data processing system. • Involved in data migration using NIFI. • Scheduled the jobs and Data-bricks workflows using Airflow. • Responsible for building solutions involving large data sets using SQL methodologies, Data

Education

• Dublin Business School

  Master of Data Analytics

  2021 — 2022

• Lindsey Wilson College

  Master of science management technology