ANUJ RAJ

Architected the Connector Convergence Project: Designed an innovative architecture enabling independent service connectors via gRPC, enhancing modularity and reducing maintenance overhead by 30%.

Optimized Data Processing and Storage: Migrated to the native Azure SDK and implemented ADLS as the primary storage, resulting in 40% faster data processing and improving file handling capabilities during job runs.

Secure Kubernetes Credential Management: Upgraded the remote credential framework to securely store Azure credentials for Kubernetes-based job runs.

Improved Data Access Performance: Implemented pagination in relational connectors, improving schema and table browsing speed for large datasets by 50%.

Error Categorization and Debugging Framework: Built a configurable system to classify user and infra errors, assign appropriate status codes, improve debugging efficiency, and ensure compliance with service SLAs and SLOs.

Advanced Data Integration: Enhanced Google BigQuery to support complex data types (Objects & Arrays), enabling advanced analytics capabilities.

Security Enhancements: Improved audit compliance by embedding userId tracking in SQL queries for DB connectors, enhancing query traceability.

Cross-Platform Compatibility: Supported Databricks 10.0 runtime (Spark 3.2), streamlining big data processing pipelines for enterprise use cases.

Expanded Connector Ecosystem: Added support for 20+ new data connectors using CData and Native JDBC, increasing platform compatibility and flexibility.

Framework Optimization: Simplified new connector integration by enhancing the JDBC framework and implementing OAuth 2.0 support.