Naveen Nannapaneni

Major Health Care Firm :

• Implemented a scalable Gen AI solution on AWS Cloud to optimize performance.
• Developed AI/ML solution to identify potential dissatisfied members and improve member rating
• Orchestrate Gen AI Data Engineering pipeline for processing terabytes of data, reducing call center traffic to ~30%
• Provide architectural guidelines to build data science solutions for 3 different projects
• Automate manual auditing workflows, saving costs and improve operational efficiencies
• Managed research initiatives in the Gen AI space using LLMs, turned them into new sales worth 500k
• Developed Data model for the enterprise wide Gen-AI solution using ERWIN
• Managed Enterprise scale data warehousing solution to support ~30+ use cases across the bank including regulatory reporting, risk, compliance & financial reporting
• Managed requirement capturing to ensure reporting needs of downstream teams
• Designed data models for data warehousing solutions using ERWIN
• Managed data needs for financial reporting
• Designed data architecture, management and governance policies to eliminate data redundancy
• Led multiple development teams and provided technical direction ensuring smooth delivery
• Proven Subject Matter Expert at various clients for business and technology needs
• Established data governance and standards for data collection, aggregation and reporting to reduce data quality issues by 40%
• Analyzed data needs to address risk, regulatory, or data management policies
• Built dashboards to pro-actively identify data quality issues
• Built master data and metadata repositories to eliminate data redundancy
• Mentored 5 junior resources
• Ensured project priorities are met.
• Major Bank in the US :
• Built a Enterprise scale Data Analytics system processing billions of viewership records supporting 20+ use cases
• Optimized data pipelines to ensure smoother execution
• Led a team of 15 data engineers and helped them with technical challenges
• Manage requirements by working closely with business enablement teams and downstream customers
• Developed Data strategy for data analytics solution
• Developed data model and monitored data governance teams using ERWIN and VS
• Enable Development teams by providing design inputs and performing code reviews
• Managed leadership communication to proactively report risks
• Develop conceptual and physical design to cater to requirements
• Developed APIs to support 3 different marketing applications.

• Built a Enterprise scale Data Analytics system processing billions of viewership records supporting 20+ use cases.
  • Optimized data pipelines to ensure smoother execution
  • Led a team of 15 data engineers and helped them with technical challenges.
  • Manage requirements by working closely with business enablement teams and downstream customers.
  • Developed Data strategy for data analytics solution
  • Developed data model and monitored data governance teams using ERWIN and VS.
  • Enable Development teams by providing design inputs and performing code reviews
  • Managed leadership communication to proactively report risks.
  • Develop conceptual and physical design to cater to requirements.
  • Developed APIs to support 3 different marketing applications.

• Design and implement ETL work flows using Hive, PySpark & Unix / Linux scripting to migrate from legacy Teradata
  system and save costs by 20%.
  •Create unit testing scripts to test the code developed to proactively reduce • issues by 60%.
  • Develop re-usable user defined functions to accelerate development by 15%.
  • Developed a generic ABC framework to process data from different sources to reduce time taken to development.

• Developed batch & streaming ETL jobs to process millions of records.
  • Developed rule engine to identify fraudulent claims to save revenue by 40%.
  • Developed subrogation workflows supporting 30+ clients of Optum.
  • Led a team of 4 developers to ensure smooth end to end delivery.
  • Built a Data quality dashboard to proactively report source data quality issues and saved time.

• Strong SQL knowledge including query design and performance tuning
  • Software development is performed using the SCRUM(Agile) methodology or Waterfall (depending of the project)
  • Develop and maintain code to cater to business requirements.