Karuna Yadav

Backend Engineer with 3+ years of experience designing scalable REST APIs, third-party integrations, and enterprise data infrastructure using Node.js, TypeScript, and Python. Built ingestion and processing pipelines handling millions of records across 8+ heterogeneous source systems; integrated with external APIs to create unified abstraction layers cutting integration effort by ∼40%. Comfortable owning systems end to end in fast-paced environments, and actively leverages AI-assisted development tools (GitHub Copilot, Claude) to accelerate delivery.

Software Engineer | Backend & AI Products at Celebal Technologies (2023-06 – Present)

• Owned end-to-end development of a backend analytics service leveraging Databricks Genie for NL-to-SQL translation, enabling natural language querying of complex enterprise datasets and reducing manual report requests to the data team.
• Designed a multi-stage request processing pipeline: SQL validation and execution, analytical result parsing, and dynamic UI configuration generation — driving the architecture from design to production deployment.
• Optimized backend performance by implementing efficient query execution flows and asynchronous result streaming, reducing latency for analytical reports by ∼35%.
• Integrated with 8+ external data source APIs, standardizing a secure abstraction layer that cut integration effort for new sources by ∼40% — directly analogous to multi-carrier API integration patterns.
• Architected an automated metadata extraction framework integrating heterogeneous enterprise sources (PostgreSQL, MySQL, Databricks) via external API connectors into a centralized processing pipeline.
• Engineered an AI-orchestration layer to automate entity classification using LLMs; managed batch processing of thousands of records with token optimization that lowered LLM API costs by ∼30%.
• Implemented a dual-sink storage architecture, mapping data into Neo4j for relationship lineage and Elasticsearch for semantic search, ensuring high-performance data retrieval.
• Built robust data transformation logic to normalize disparate metadata formats, ensuring integrity and traceability across the enterprise data ecosystem.
• Collaborated closely with Data Engineering and Product teams to align backend architecture with technical specifications and business requirements, navigating ambiguous requirements and evolving priorities.
• Participated in code reviews, focusing on maintainability, security, and clean coding standards across Python and TypeScript/Node.js services, including clear engineering documentation.
• Ensured stable production deployments through rigorous testing, CI/CD pipeline monitoring, and proactive debugging, helping maintain 99.9%+ service uptime.

Bachelor of Technology - Computer Science (2019-2023)