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Kamalesh Ponnivalavan
Mid-level Data Engineer specializing in capital markets post-trade data platforms
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Data/streaming engineer in capital markets who led an end-to-end trade settlement data product (Kafka→MongoDB→data lake) with rigorous data-quality logic and ~$175K first-year operational impact. Also built a low-latency Go-based CME market data engine feeding SOFR curve generation, using MSK on EKS with performance tuning (idempotency, compression, partitioning) to achieve sub-100ms delivery.
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Key Strengths
- Led end-to-end design and development of an EOD settlement efficiency feed/pipeline
- Built hourly open-trades snapshot with strong data-quality rules (latest-message per trade; remove settled/cancelled)
- Stakeholder-driven requirements discovery in ambiguous domain (collaborated with data provider, business users, SMEs, operations)
- Delivered measurable business impact (~$175K first-year impact) via improved settlement operations
- Designed low-latency real-time market data ingestion in Go with sub-100ms delivery to multiple consumers
- Performance engineering for streaming systems (goroutines, per-product load isolation, backpressure handling, zero-copy, Kafka/MSK tuning)
- Reliability/correctness controls in streaming (timestamp reordering, Kafka idempotency, partitioning by instrument/product)
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