Rohit Uppalapati

• Contributed to backend platform engineering by developing ETL data pipelines.
• Responsible for the design, development, and maintenance of ETL pipelines for all company customers as part of the platform engineering team.
• Developed a robust Loader platform capable of handling ETL processes to convert CRM data into time series, based on specified write schema.
• Designed and implemented a framework for processing custom formulas used by CRM customers, capturing formula changes and stage transitions.
• Created pipelines for computing and storing time series data by utilizing formulas.
• Created and optimized processes for generating dynamic indexes on Postgres tables by considering schema changes and ingested data.
• Implemented optimization techniques on dimension and fact tables, resulting in a 40% reduction in cluster load.
• Configured pipelines to support application teams, maintained customer data in data warehouses, and enhanced ETL pipelines.
• Monitored and created performance dashboards in Sumo Logic and Datadog

Snowflake based Enterprise Data Platform:

• Member of the Architecture team responsible for designing the enterprise data platform supporting Business analytics applications, utilizing technologies such as Snowflake, AWS Lambda, AWS S3, AWS Glue, and NiFi.
• Developed NiFi pipelines and Python scripts for loading data from on-prem data lake to AWS S3 staging area.
• Designed and implemented AWS Lambda function workflows for executing Snowflake stored procedures.
• Developed Terraform templates for resource deployment in a CI/CD pipeline.

Enterprise Data Ingestion Framework:

• Led development of an enterprise data ingestion tool, ensuring seamless and source agnostic data ingestion into the company's data lake.
• Implemented improvements in the application, including developing API endpoints using Java and Spring Boot.
• Developed NiFi templates to complete file ingestion, database ingestion for different sources and destinations, and PySpark jobs for data profiling and validation.
• Managed Cloudera Hadoop cluster for seamless execution of Spark jobs and maintained application metadata using Azure SQL.

Advanced Analytics: Evidence-Based Analysis of Network Data.

• Created a graph model that determines connections between applications and servers based on network data from the data center.
• Developed a data model in Python and Neo4j to extract application-server relationships by analyzing the interconnectedness of applications and servers.

• Provided support to the Sepsis Platform team, contributing to the successful migration and enhancements of the platform's architecture.
• Designed and developed a company-wide enterprise application for detecting open-source dependencies and notifying relevant departments about available updates.
• Developed real-time data cleansing, normalization and standardization techniques for big health information datasets sourced from multiple platforms using Apache Hadoop (Cloudera’s CDH) Map/Reduce and Apache Storm technologies. Loaded the cleaned data in HBase and HDFS effectively.
• Implemented Apache Crunch Pipelines for data preprocessing and removal of outliers, enhancing accuracy of analytics.
• Designed and developed Crunch pipelines to evaluate patients' similarities and correlations.
• Hands-on experience with Hive, Hue, Splunk and automation of Hadoop jobs.