Suryapavan Chandra

• Served as one of the core engineers in building NABARD's enterprise Data Governance Platform from the ground up, driving architecture, onboarding strategy, and platform adoption.
• Led a team of 3 Data Engineers and collaborated with client stakeholders to deliver enterprise-scale data engineering and governance initiatives.
• Designed and developed scalable data ingestion, metadata processing, and governance workflows using Databricks, PySpark, Spark SQL, Delta Lake, and Unity Catalog for NABARD's enterprise data platform.
• Integrated 32+ enterprise applications across MySQL, PostgreSQL, SQL Server, Oracle, and AWS S3 into a centralized Databricks Lakehouse, enabling unified data visibility and governance.
• Executed large-scale metadata onboarding and cataloging processes, generating 100,000+ datasets and cataloging 1M+ columns through automated Databricks pipelines.
• Built and optimized ETL/ELT pipelines using PySpark and Spark SQL, improving metadata processing performance and scalability across enterprise systems.
• Designed and published 200+ governed data products, delivering trusted, analytics-ready datasets for business users and reporting platforms.
• Implemented PII discovery, classification, and masking for 200K+ sensitive data fields, strengthening enterprise data security, privacy, and compliance.
• Developed advanced Spark SQL and analytical queries for data validation, profiling, reconciliation, and metadata analysis, ensuring high data quality standards.
• Leveraged LLM-powered classification and governance automation to accelerate sensitive data identification and business glossary enrichment.
• Owned the end-to-end lifecycle of governance assets, including dataset onboarding, metadata enrichment, lineage validation, business glossary mapping, and governed data product delivery.

• Developed and maintained ETL pipelines using Python, SQL, and Databricks for HRMS and Healthcare applications.
• Processed and managed 4,000+ datasets, enabling efficient analytics, reporting, and business intelligence workflows.
• Performed data profiling, cleansing, standardization, and quality checks to ensure reliable and accurate data processing.
• Built and optimized SQL queries and backend integration services to support cross-application data exchange.
• Developed REST APIs and integration services for seamless data movement across multiple applications.
• Stored and managed transformed datasets in MySQL and PostgreSQL databases supporting reporting and operational platforms.
• Monitored and maintained Linux-based production ETL environments, troubleshooting issues and improving system performance.
• Resolved production incidents and performance bottlenecks, ensuring high availability and reliability of critical applications.
• Automated deployment and release activities, contributing to streamlined delivery and operational efficiency.
• Executed client deployment activities for FauxIQ Technologies, ensuring successful implementation and production readiness.

Client Deployment: FauxIQ Technologies (Apr 2025 – Aug 2025)