Ajeet Kumar

• Designed and enhanced distributed ETL pipelines using Python, PySpark, PyFlink, Hive, Trino, SQL, MySQL, and AWS S3, delivering end-to-end ingestion for the Ziplabs Leadership pipeline integrating external datasets into the Redux → MPD platform. • Resolved 20+ application security vulnerabilities including SQL Injection, Weak Hash, Weak Random, and Path Traversal across Python services using SAST, taint-flow analysis, secure coding practices, parameterized SQL, and cryptographically secure random generation. • Performed Software Composition Analysis (SCA) by assessing CVEs, validating exploitability, upgrading vulnerable dependencies, implementing automated data quality checks, and collaborating with Security and Data Engineering teams to resolve security findings. • Delivered 14+ production pull requests covering security remediation, dependency upgrades, parser enhancements, and ETL improvements while leveraging Claude Code, Devin, and Windsurf for development, debugging, code reviews, and technical documentation.

• Developed scalable backend services and REST APIs using Python, AWS Lambda, and Aurora PostgreSQL to support real-time retail operations. • Designed APIs and data models for the Store Control Dashboard, enabling real-time operational monitoring and business analytics. • Optimized backend processing and SQL queries, improving execution performance by nearly 40% through query and processing optimizations. • Integrated GraphQL, Aurora PostgreSQL, Amazon Redshift, and AWS services to build scalable backend and data solutions for retail operations.

• Client: Uber Technologies — Uber for Business Jan 2025 – Nov 2025 ∗ Built end-to-end data ingestion pipelines using Python, Pandas, SFTP, and Amazon S3 to ingest partner CSV datasets and process JSON-based configurations for employee transportation workflows. ∗ Developed Python, SQL, and Trino workflows to clean, transform, validate, and reconcile partner data into system-compatible formats, ensuring data quality and consistency. ∗ Processed transportation datasets through downstream business models to support cab location allocation, minimizing route deviation and improving operational efficiency across employee transportation services. ∗ Automated report generation and email delivery using Google Drive and Mail APIs, supporting production onboarding, troubleshooting, and launches across 10+ cities. • Client: Saison Omni — Loan Management System (FinTech) Jul 2022 – Jan 2025 ∗ Built scalable ETL pipelines and financial data workflows using Python, PySpark, SQL, PostgreSQL, and AWS for enterprise Loan Management Systems. ∗ Led large-scale financial data migration with automated data cleansing, validation, and reconciliation, achieving 99.93% migration accuracy. ∗ Designed data validation and partner onboarding frameworks to improve data quality, reporting accuracy, reconciliation, and month-end financial book closure. ∗ Optimized PostgreSQL queries, AWS infrastructure, and reporting workflows, improving system reliability and reducing manual effort across distributed financial systems.