Python application developer and iMigrator platform owner at Cognizant, focused on application development and maintenance, Python automation tools, and Jenkins-scheduled execution of long-running jobs. Improved benchmark processing by up to 3x using Polars and DuckDB and resolved low-memory failures. Built a shared-folder, three-tier application updater and co-developed Snowflake pushdown reconciliation and AWS Bedrock / Claude diagnostics. iMigrator supports 15+ database platforms and 12+ data formats; my connector contributions are listed in the experience details.
📍 Kolkata, India
Engineered for high-throughput distributed computation, heterogeneous cloud databases, GenAI pipelines, and autonomous test harnesses.
Production-grade systems programming, AST parsing, high-concurrency multiprocessing, and Windows OS API automation.
Memory-optimized columnar computation engines replacing traditional memory-bound iterations with zero-OOM processing for 50M+ rows.
Deep daily integration of frontier AI coding tools, autonomous agents, and conversational intelligence across VS Code and terminal workflows.
Product capability list for iMigrator. Personal connector contributions are described in the experience section; this list does not imply practical SQL or administration experience on every database.
Product capability list for iMigrator, not a claim of hands-on experience with every listed format. Practical Excel and ETL experience is described in the experience section.
Autonomous failure clustering and root-cause diagnostics integrating cloud foundational models for automated reconciliation.
Jenkins jobs for scheduled, long-running Python automation on higher-resource CI servers, with email results; PyInstaller-packaged desktop applications and shared-folder self-updates.
Enterprise QA orchestration, automated defect lifecycle management, DDL schema drift tracking, and Informatica pipelines.
Proven product ownership, architectural migrations, and production-grade engineering at Cognizant.
Core platforms, autonomous assistants, and enterprise distribution systems engineered for high scale.
High-throughput enterprise data reconciliation platform adopted across multiple projects. Stabilised the inherited core pipeline and independently introduced DuckDB-based validation, delivering up to 3x processing speed in benchmark runs and eliminating crashes on low-RAM systems.
• Platform Ownership & Connector Integrations: Own and maintain iMigrator across multiple projects; added or extended MongoDB, Snowflake token-based authentication, CTRL-file, and DB2 integrations within a product supporting 15+ platforms.
• In-Warehouse Pushdown Reconciliation: Co-developed cross-database pushdown reconciliation staging heterogeneous source data into temporary Snowflake tables for distributed in-warehouse joins.
• Chunked Processing & Stability: Improved and stabilised inherited chunked processing routines and dynamic memory controls, eliminating crashes on user workstations.
• Keyless Fallback & Safety: Owned and maintained keyless fallback reconciliation routines and automated DML execution safeguards.
• GenAI Failure Diagnostics: Co-developed autonomous diagnostics with AWS Bedrock / Claude Sonnet, clustering mismatch patterns and returning structured JSON root-cause classifications.
• Reporting & Packaging: Improved HTML validation reports with enhanced console logging and fallbacks; built standalone self-updaters and packaging via PyInstaller.
Autonomous multi-modal data engineering agent built with the Google ADK during Google's 4-Hour "Build with Gemini" hackathon; earned the Google Developers Track 3 badge.
• Dynamic Schema Catalog: Automated Firestore schema registration cataloging database schemas, primary keys, and data types across heterogeneous sources (list_tables, get_table_details, add_table).
• Anti-Pattern Detection: AST parsing with sqlparse to detect full table scans, missing filters, and uncapped sorting with Snowflake, BigQuery, and PostgreSQL optimizations.
• Multi-Modal Architecture Generation: Produced visual ER diagrams and animated Kafka event-streaming architecture diagrams leveraging Gemini multimodal models on Vertex AI with GCS storage.
• Vertex AI Memory Bank: Integrated PreloadMemoryTool for session-level dialect persistence, paired with AgentEngineSandboxCodeExecutor and a responsive FastAPI/A2UI card interface.
Independently built a no-additional-hosting-or-licensing-cost updater that uses an existing shared folder and release/version information to detect, verify, and stage newer application builds through a three-tier handoff: Launcher → Updater → Production Runtime.
• Self-Update Flow: The launcher hands control to the separate updater; it checks shared-folder release data, verifies a newer build, and replaces the application without asking a running process to overwrite its own locked files.
• Rollback & Distribution: Uses an existing shared folder for distribution, avoiding additional updater hosting or licensing costs; build verification and rollback safeguards protect the replacement.
• Zero Console Flashing: Leveraged Windows API process handling to provide silent execution with clean SQLite telemetry logging and network distribution.
• CLI Maintenance Utility: Authored automated CLI diagnostics and repair utilities for rapid environment health checks and client distribution audits.
Production ETL integration pipeline processing fixed-width and comma-delimited healthcare feeds into dimensional Oracle tables with automated XML welcome letter distribution.
• Multi-Feed Processing: Cleaned, validated, and normalized multi-tier patient datasets across complex business transformations.
• Schema Compliance: Validated outgoing XML welcome records against strict enterprise XSD schema definitions.
Verified industry credentials, certifications, and academic foundations.
Specializing in Data Engineering, High-Throughput Reconciliation, ETL Automation, and AI-Driven Data Systems. Let's discuss modern data pipelines, Polars, DuckDB, or generative AI architecture.