Goutham Kumar J Email & Phone Number

• Involved in all phases of SDLC from requirement gathering, design, development, testing, Production, user training and support for production environment.• Design, review, implement and optimize data transformation processes in Oracle, Hadoop(primary) utilizing Python, and Hive.• Collaborate Solution Architects, Principal Engineers, DevOps team, & Business Analysts to understand the precise business needs of the acceptance criteria and ensuring the deliverables of data products.• Used Enterprise GitHub Repos for version control.• Designed and Developed ETL jobs to extract data from Salesforce replica and load it in data martin Redshift.• Responsible for extracting, transforming, maintaining quality, and ensuring Integrity of data utilizing Big Data technologies like Hadoop, MapReduce, Pig, Hive, Flume, Sqoop, Spark and AWS cloud services like S3, Lambda, EC2, EMR, RedShift, DynamoDB, Glue.• Involved in designing, optimizing Spark SQL queries, Data frames, import data from Data sources, perform transformations, perform read/write operations, save the results to output directory into HDFS.

• Involved in complete SDLC – Requirement Analysis, Development, Testing and Deployments.• Responsible for Data Ingestion, Data Cleansing and Data Transformation.• Working with Python 3.8, AWS Serverless stack.• Built infrastructure, networks, and systems for scalability, resiliency, availability, and recovery though infrastructure-as-code.• Use Terraform - Infrastructure as a code.• Used terraform to Launch EMR and EC2 servers and install software and services on servers.• Identity and Access Management to various AWS services and Accounts using Terraform.• Focused on building VPCs from scratch and using AWS CloudFormation, creating private and public subnets, security groups, network access lists, configuring internet gateways, OpenVPN, creating AMI, understanding of user access management/role-based access/multi factor authentication, API access.• Used SQL on the reports generated by companies to judge the complexity of data and datatypes.• Created DynamoDB tables to load large sets of structured, semi-structured and unstructured data coming from UNIX, NoSQL, and a variety of portfolios

• Deploying, automating, maintaining and managing AWS cloud-based production system, to ensure the availability, performance, scalability and security of productions systems. • Setting the Infrastructure as a code (IaaS) in the AWS cloud platform from the scratch through cloud formation templates by configuring and integrating appropriate AWS services as per the business requirement. • Preformation critical patches to the windows servers on a monthly basis through the AWS Patch manager. • Designed and implemented the data migration from the SQL Server (On-Prem) to AWS Cloud (Redshift) which resulted in huge cost saving for the organization. • Building data model and schema designing based on the business requirement and interacting with different clients to efficiently design and model. • Working with SQL queries in order QC the load on the DB with the ETL tool and managing appropriate changes to tables for query performance improvement. • Knowledge and understanding of the AWS Sagemaker for the ML model creation. • Working with the automation of transient AWS EMR cluster for huge data processing monthly in order to cost control to the organization and efficiently using the power of the AWS EMR.

• Developed an end-to-end data pipeline solution in AWS cloud which reads data from Redshift, performs transformations using AWS Glue(spark) and writes data to neo4j, which is based on SQS events from upstream data.• Orchestration of glue jobs using step functions, RDS and DynamoDB for audit purpose, and lambdas to invoke step function and python code for RDS database updates and queries.• Develop Glue jobs with Python code to query data from Aurora DB and create tables in Redshift.• Work with PySpark classes, traits, objects using data frame and RDD in Spark for Data Aggregation, queries and writing data back into graphical database neo4j.• Created and managed S3 buckets in AWS and imported data from AWS S3 into Spark RDD and performed transformations on RDD.

• Performed Column Mapping, Data Mapping and Maintained Data Models and Data Dictionaries. • Built system to perform real-time data processing using Spark streaming and Kafka. • Involved in retrieving multi-million records for data loads using SSIS and by querying against Heterogeneous Data Sources like SQL Server, Oracle, Text files and some Legacy systems. • Expertise in using different Transformations like Lookups, Derived Column, Merge Join, Fuzzy Lookup, For Loop, For Each Loop, Conditional Split, Union all, Script component etc. • Transferred data from various data sources/business systems including MS Excel, MS Access, and Flat Files to SQL Server using SSIS/DTS packages using various features. • Involved in Performance tuning of ETL transformations, data validations and stored procedures. • Strong experience in designing and implementing ETL packages using SSIS for integrating data using OLE DB connection from heterogeneous sources. • Developed and designed system to collect data from multiple portals using Kafka and then process it using spark. • Extensively worked on UNIX Shell Scripting for splitting group of files to various small files. • Analyzed the SQL scripts and designed it by using PySpark SQL for faster performance. • Created roles using SSAS to restrict cube properties.