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Kaushik Sriram
Mid-level Software Engineer specializing in event-driven FinTech backend systems
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Senior/Staff-level backend/platform engineer who owned Stripe’s global payout settlement system end-to-end, building an event-driven Python/Kafka platform processing millions of events daily across 30+ countries. Deep experience operating high-reliability distributed systems in production (incidents, replays/backfills, schema evolution, observability) and scaling on AWS/EKS with strong testing and deployment practices.
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Key Strengths
- Owned and operated a global payout settlement system end-to-end in production
- Scaled event-driven platform to millions of events/day across 30+ countries
- Strong incident response: monitoring/alerting, root-cause analysis, hotfixes, postmortems
- Resilience patterns for unreliable external APIs (retries, fallbacks, replay without data loss)
- High-volume streaming pipeline design (Kafka + Spark) with schema evolution and idempotency
- Pragmatic distributed-systems tradeoffs (eventual consistency for non-critical data)
- Built strong observability and testing practices enabling fast production recovery (e.g., rollback and fix within ~1 hour)
- Owned end-to-end production feature across frontend, backend, data pipeline, and monitoring (global payout settlement monitoring)
- Built real-time streaming pipeline processing millions of settlement events/day (Kafka + Spark)
- Measurable ops impact: reduced payout settlement delays 25% and manual reconciliation time 30%
- Designed reliability mechanisms for event-driven systems (retries, exception handling, idempotency, out-of-order event handling)
- Production monitoring/alerting design with AWS CloudWatch and anomaly-based alerts; created runbooks for on-call
- Productionized ML anomaly detection service with evaluation (precision/recall) and guardrails; reduced missed issues by 30%
- Incremental legacy modernization with safe rollout strategies (feature flags, canary releases, parallel runs) and zero downtime/regressions
- Strong cross-functional partnership with treasury/risk/finance and CTO-level stakeholders; translated ambiguous goals into phased roadmap
- Effective embedded execution across multiple teams with minimal oversight; shipped in weeks via incremental delivery and async communication
- Used AI dev tools beyond autocomplete to accelerate refactors/debugging; reduced refactor time ~30%
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