t
tarak_r

Taraka R

@tarak_r

Software Engineer Java Backend Distributed Systems Fintech

India
Engels, Hindi, Tamil, Telugu
Sommige informatie wordt in het Engels weergegeven.
Over mij
I am a backend engineer with 3.5+ years of experience building payment, onboarding, and KYC systems for Indian fintech. I specialize in Spring Boot microservices, distributed systems, and event-driven architecture using Kafka and AWS. I have a proven track record of optimizing database performance and ensuring exactly-once payment semantics in high-scale production environments.... Lees meer

Skills

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tarak_r
Taraka R
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Van website naar app
I will develop scalable java spring boot rest api backend for your app

Werkervaring

Zetasoft

SOFTWARE DEVELOPMENT ENGINEER - 2

Zetasoft • Fulltime

Dec 2024 - Present1 yr 9 mos

FD-backed credit cards for India's underserved credit segment. I own the onboarding, KYC and payments backend. Own the application-journey state machine that moves a customer from sign-up to card issuance across 15+ steps. Authored the HLD/LLD, drove the API contracts, and shipped it to production. Resolved a P0 database hotspot on the high-write user _journey_ event table that was blocking product launches. Separated the transactional and reporting paths — moved to a partitioned schema and pushed analytics consumers onto SNS→SQS fan-out instead of reading off the write path. Cut average write latency ~60%. Built the Autopay/e-Mandate payments pipeline (NDPS, Razorpay) for one-time charges, mandate setup and recurring debits. Transactional outbox plus idempotency keys guarantee a charge is applied exactly once under retry storms, duplicate webhooks, or partial failure between our DB and the gateway. Integrated IOB core-banking APIs (CIF and card lifecycle) over OAuth 2.0 with AES/RSA-encrypted payloads, and built a centralized retry framework (exponential backoff + idempotency keys) that recovered ~30% of previously failed applications with zero manual intervention. Reduced p95 latency on critical onboarding endpoints from 800ms to ~250ms (~70%) through composite indexing, selective denormalization, MyBatis batch operations, and Redis caching on hot read paths. Built the VKYC pipeline with webhook callbacks, asymmetric encryption, Resilience4j time-based circuit breakers, and ShedLock-coordinated refund schedulers so jobs fire exactly once across a multi-pod deployment — eliminated dropped refunds. Run the services on EKS: deployment manifests, resource limits and autoscaling, rolling releases, and pod-level debugging during incidents. Tuned Datadog APM with error.expected span tagging for expected business outcomes — cut the false-positive error rate ~40%, stabilized SLO burn-rate alerts, and reduced on-call paging. Drive code reviews, LLD walkthroughs and incident