WalletPe
Personal fintech-inspired wallet architecture exploring event-driven state machines, Apache Kafka integration, and the Transactional Outbox pattern to solve dual-write inconsistency.
Backend engineer · Pune, India
I'm Harveer, a Java backend engineer at Infosys. I build secure APIs, event-driven services, and the systems teams trust in production.

Systems Engineer · Infosys
Pune, Maharashtra
Selected work
A sample of the problems I've designed for: correctness, throughput, and easier operations.
Personal fintech-inspired wallet architecture exploring event-driven state machines, Apache Kafka integration, and the Transactional Outbox pattern to solve dual-write inconsistency.
High-throughput rule validation engine replacing manual compliance checks, reducing audit cycle times from 4-5 hours to 10-15 minutes (Infosys Insta Award Winner).
Production-oriented medical appointment scheduling engine engineered in Spring Boot and PostgreSQL, utilizing pessimistic row-level locking to eliminate booking collisions under heavy contention.
Production APIs, integrations, and automation at scale.
Aug 2025 — Present
Pune, Maharashtra
Core backend engineering for high-throughput B2B ordering and milestone integration platform connecting 650+ downstream telecom enterprise systems.
Problem solving
Competitive programming keeps my approach to algorithms, complexity, and edge cases deliberate.
Visit LeetCode profile500+
problems solved
1734
peak contest rating
80–90%
audit effort reduced
Capabilities
I care about the full path: service design, data integrity, asynchronous workflows, and reliable delivery.
Core programming and query languages used for backend logic and storage access.
Enterprise Java framework ecosystem and RESTful service design.
Asynchronous processing, event pipelines, and distributed consistency patterns.
Relational data stores, in-memory caches, and migration tooling.
Containerization, automated testing, continuous integration, and server runtimes.
Computer science theory, algorithm complexity, and high-level architectural patterns.
Concurrency, transactions, event-driven systems, and the engineering decisions behind them.
How to avoid data inconsistency between relational databases and Kafka event streams in high-throughput Spring Boot architectures without 2-Phase Commit.
When to choose row-level locking over versioned retry loops in burst concurrency scenarios like medical scheduling and flash booking.
Standardizing API error contracts across 650+ downstream systems using Spring Boot @RestControllerAdvice and TeleManagement Forum specifications.