Khushi Oswal

Software Engineer

Pune, Maharashtra, India2 yrs experience

Key Highlights

Designed APIs processing 100k+ records daily.
Improved API latency by 15-20% through optimization.
Automated data profiling, reducing manual effort by 50%.

Stackforce AI infers this person is a Backend Engineer specializing in SaaS solutions with a focus on data-intensive systems.

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Skills

Core Skills

JavaSpring BootPython

Other Skills

RESTful APIsSQLCachingData ValidationData TransformationDead-Letter QueuesRetry StrategiesCross-Functional CollaborationpandasNumPyCSV/Excel processingDockerPython (Programming Language)Spring SecurityMongoDB

About

Backend Engineer with hands‑on experience designing, building, and optimizing Java/Spring Boot services in production environments. I focus on RESTful API design, SQL data modeling, caching strategies, and performance optimization to improve system latency, data processing efficiency, and backend reliability at enterprise scale. At ZS Associates, I work on a Master Data Management (MDM) enhancement platform, where I design and optimize Java/Spring Boot APIs integrating with the Reltio MDM platform and downstream systems processing 100k+ records per day. My work includes data validation and transformation, improving API latency through caching and query optimization, and building resilient failure‑handling using DLQs and retry strategies. Open to Backend Engineer / Java Developer roles across India or remote, where I can work on scalable microservices, data‑intensive systems, and cloud‑native architectures using Java, Spring Boot, and modern DevOps tooling.

Experience

2 yrs

Total Experience

2 yrs

Average Tenure

2 yrs

Current Experience

Zs

Software Engineer

Jun 2024 – Present · 2 yrs · Pune, Maharashtra, India · Hybrid

MDm Enhancement Platform
Designed RESTful APIs using Java and Spring Boot to integrate client applications with the Reltio MDM platform, enabling reliable data synchronization across multiple downstream systems and processing around 100k+ master data records per day.
Owned backend data validation, cleansing, and transformation workflows using Java utilities and Reltio rules on datasets with millions of records, reducing manual data correction effort by 2–3 hours per week.
Improved API response latency by 15–20% by introducing caching, optimizing SQL queries, and removing redundant service calls in high‑traffic MDM workflows handling thousands of requests per day.
Implemented failure‑handling mechanisms using dead‑letter queues and exponential backoff retry strategies for batch data uploads, preventing pipeline blockage and increasing resilience to transient API and data failures.
Collaborated with cross‑functional teams to debug production issues, improve API reliability, and ensure consistent data quality across environments.
Data Analyzer Tool
Built and deployed a Python‑based backend analysis tool to estimate data transformation effort and detect data inconsistencies across large client datasets (10k+ records per run), improving estimation accuracy to above 80%.
Automated data profiling and summary report generation using Python (pandas, NumPy, CSV/Excel processing), reducing manual assessment time by roughly 50%.