Mounika Baddam

About Mounika Baddam

With over seven years of professional experience, I specialize in big data engineering, leveraging tools like AWS, Hadoop, and Spark to drive scalable data solutions. At AT & T, I focus on enabling seamless data processing and automation to support business objectives. Motivated by a passion for problem-solving and innovation, I aim to contribute diverse perspectives and technical expertise to deliver transformative results. At AT & T, I work on configuring AWS EMR clusters, automating workflows with Apache Airflow, and optimizing Spark clusters for enhanced performance. My role involves translating client needs into functional specifications, onboarding them onto the Hadoop ecosystem, and ensuring efficient data workflows using tools like IAM, Lambda, and S3. These contributions enable our team to deliver robust and reliable data-driven solutions.

Experience

1. Sr Data Engineer

   AT&T

   Jun 2023 — Present

   Worked on setting up and configuring AWS\'s EMR Clusters and Used Amazon IAM to grant fine-grained access to AWS resources to users• Very good implementation experience of Object-Oriented concepts, Multithreading and Java/Scala• Responsible for estimating the cluster size, monitoring and troubleshooting of the Spark data bricks cluster• Automated resulting scripts and workflow using Apache Airflow and shell scripting to ensure daily execution in production.• Evaluating client needs and translating their business requirement to functional specifications thereby onboarding them onto Hadoop ecosystem. • Installed application on AWS EC2 instances and configured the storage on S3 buckets. • Used the AWS-CLI to suspend an AWS Lambda function. Used AWS CLI to automate backups of ephemeral data-stores to S3 buckets, EBS.• Developed Java Map Reduce programs for the analysis of sample log file stored in cluster.• Developed custom Kafka producer and consumer for different publishing and subscribing to Kafka topics. • Migrated Map reduce jobs to Spark jobs to achieve better performance.• Used AWS Data Pipeline to schedule an Amazon EMR cluster to clean and process web server logs stored in Amazon S3 bucket.• Written the Map Reduce programs, Hive UDFs in Java• Extracted and updated the data into HDFS using Sqoop import and export. • Developed a Spark job in Java which indexes data into ElasticSearch from external Hive tables which are in HDFS.• Implemented Data Quality in ETL Tool Talend and having good knowledge in Data Warehousing• Working on designing the Map Reduce and Yarn flow and writing Map Reduce scripts, performance tuning and debugging. • Created Airflow Scheduling scripts in Python• Exported Data into Snowflake by creating Staging Tables to load Data of different files from Amazon S3.• Installed Kafka Producer on different severs and Scheduled to produce data for every 10 seconds

Education

• JNTU UNIVERSITY

  B.TECH