Prashant Rane

Cloud Data Architect / Engineer

• Contributed to the architecture and development of a Python-based automated data validation framework to streamline data quality checks across cloud pipelines
• Led multiple proof-of-concept (PoC) initiatives utilizing Azure Data Factory, Synapse Analytics, and Databricks to define the migration strategy from on-premises data warehouses to the Azure cloud ecosystem
• Designed and implemented Databricks and Synapse notebooks to assess and drive the migration approach for legacy Informatica mappings and on-prem enterprise data warehouse (EDW) workloads
• Collaborated in cross-functional architectural discussions to define CI/CD practices and establish version control standards using Azure DevOps for data pipeline development and deployment

ETL Solution Architect

• Designed and implemented end-to-end ETL solutions using Informatica PowerCenter, ensuring data integrity, performance, and scalability
• Created detailed source-to-target mapping (STM) documents to support development and maintain traceability of business requirements
• Authored comprehensive application process flow documentation
• Provided technical leadership and mentorship to ETL developers, working closely with solution architects to deliver high-quality data integration artifacts
• Participated in requirement elicitation and collaborated with BAs and SMEs to review and finalize mapping specifications
• Conducted in-depth data scenario analysis and facilitated brainstorming sessions with business analysts and key stakeholders to validate requirements and edge cases
• Led and participated in code review sessions and Supported QA and UAT testing by providing technical expertise to ensure code quality
• Delivered walkthrough of data flows and application processes to business users and prod support teams, and prepared Standard Operating Procedures (SOPs)

Accomplishments:

• Extract Process Automation: Designed ETL process automation framework to generate data extracts for Self-Funded line of business. This framework provided standard requirement collection process, and developed steps that reduced 80% of design, development and testing efforts.
• QA test automation (Python): Designed Pythonic framework to test data extracts for all line of businesses. Which improves the quality of testing, reduced the defects to production and saves at least 50% of Unit testing and QA testing efforts.