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Jehanzeb Khan
Director-level Engineering Manager specializing in large-scale data and compute platforms
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About
Platform and distributed-systems leader (player-coach) who owned architecture and reliability for an Amazon analytics/data platform serving ~100K internal users at exabyte scale. Built an ML-driven “Lakeflow” optimization layer that cut pipeline completion times ~20–25% and reduced compute waste >15%, and led major incident response/redesign efforts (e.g., deletion storm) with strong rollout/observability/rollback practices.
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Key Strengths
- Led architecture and migration of a large-scale analytics/data platform serving ~100K internal users on ~1 exabyte active data
- End-to-end ownership across architecture, implementation, and high-sensitivity code reviews (reliability, failure handling, data consistency)
- Safe production change management: staged rollouts, strong observability, and automated rollback to avoid disruption at scale
- Turned ambiguous stakeholder request into shipped ML-driven optimization layer with measurable impact (20–25% faster pipeline completion; >15% compute waste reduction)
- Owned major production incident end-to-end (“OD3 delete storm”): coordinated multi-team response; redesigned deletion workflow with guardrails (rate limiting, backpressure, isolation) to prevent recurrence
- Player-coach leadership with sustained technical depth (60–70% management / 30–40% hands-on) focused on high-leverage architecture and incident debugging
- People management at scale: led multi-geo org of 20+ engineers; structured 1:1s, career plans, coaching, and handled underperformance including managed exit
- Improved delivery speed and quality via lightweight process: shift-left validation, automated CI gates, feature flags/cohort rollouts; results included ~20% faster builds and ~30% fewer escaped defects
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