# Jay Pan

> Staff Backend Engineer | Microservices & Recommendation APIs | Delivering 0→1 Consumer Features

Location: San Jose, California, United States
Profile: https://flows.cv/jaypan

> Staff Software engineer (14+ yrs) building low-latency, high-scale microservices, petabyte-scale data pipelines, and ML-powered recommendation APIs in Java/Scala, Spark EMR, DynamoDB, SageMaker, Kinesis, ECS Fargate.
> 0→1 consumer features: principal architect for Amazon Inspire and menu-level personalization—scaled to 2M DAU / 25M uniques, drove $67M+ incremental annual revenue, while sustaining p99 <150 ms at multi-K TPS.
> APIs that power UI: owned versioned contracts for iOS/Android and built server-driven mobile UI to ship without app-store releases—cut feature lead time 14 days → <24 h; personalization API delivered p99 <75 ms at ~2K TPS with ~40% lower cost.
> Product rigor with data: partnered with PM/Design/Data Science to run A/Bs across CTR→PDP→purchase; launched new verticals via config (YAML), yielding +50% CTR, +2% revenue (~$35M), and ~80% fewer VP escalations.
> Operate & lead: IaC-first (CDK), canaries, auto-rollback, and focused SLOs for 99.99% availability; led 25+ engineers across six orgs and mentored L5/L6s into domain leads.

## Work Experience
### Staff Software Engineer @ Amazon
Jan 2016 – Present
Inspirational social shopping experience (Inspire Tab)
● Inspire is a TikTok-like shoppable video and photo feed in the Amazon retail app, enabling customers to discover and buy products through a personalized recommendation-driven experience.
● End-to-end technical ownership of the feed stack. Defined the technical vision and led 25+ engineers across six orgs to deliver candidate-generation services, Kinesis/Spark ingestion, multi-stage ranking, and iOS/Android presentation layers while preserving <300 ms p99 end-to-end latency at 3 K TPS peak.
● Petabyte-scale hybrid pipeline. Designed a parallel batch-and-stream flow (Kinesis → S3 → Spark EMR) that cut feature-store refresh SLA from 24 h to 2 h, enabling same-day trend capture.
● Multi-level ranking platform. Built a configurable recommender model with offline candidate retrieval, SageMaker-hosted scorer that blends collaborative-filter signals with business rules for online candidate ranking and generation.
● Microservice for candidate generation. Authored a Java/Scala service with pagination, de-dup, and interest‐based filtering; runs in ECS Fargate, autoscaling to 3 K TPS with p99 <300 ms.
● Business impact. Scaled to 2 M DAU, 25 M uniques, and $32 M annual GMV while keeping p99 E2E latency < 150 ms. 

>>>>> Amazon Programs and Features Personalization
● Built the platform that tailors Amazon programs (Deals, Prime, Subscribe-&-Save) in the navigation menu and homepage using ML-driven ranking.
● Full data stack ownership. Ingested billions of click & view events via Kinesis → S3 → Spark; hourly roll-ups and compaction trimmed I/O cost 60 % and cut pipeline wall-time 70 %.
● AWS-native personalization API. Java + ECS front end reads pre-computed candidate lists from DynamoDB and applies lightweight real-time rules; p99 < 75 ms at 2 K TPS while 40 % cheaper than always-on SageMaker inference.
● Impact. Personalized menu lifted CTR 50 %, drove +2 % revenue (+$35 M annualized) and cut VP escalation tickets 80 %.

### Software Developer @ Brocade
Jan 2010 – Jan 2016 | San Jose, California, United States
● Worked on Brocade VCS cluster platform; delivered distributed control-plane services that power flat, scalable data-center fabrics.
● Designed “logical-chassis” architecture so any node can configure the entire 40-switch fabric—cutting provisioning effort ~80 %.
● .Implemented fast state-sync and transaction back-end (C++/Java), reaching sub-second convergence on topology changes.
● Published REST / NETCONF APIs with YANG models consumed by OpenStack & VMware for self-service automation..
● Rules-based API contract. Developed a standardized API and logic evaluation engine, abstracting complex conditional workflows, ensuring backward compatibility with 10+ legacy app versions and multi platform compatibility.


## Education
### Masters in Computer Science
University of Southern California

### Bachelor of Technology in ECE
Vellore Institute of Technology


## Contact & Social
- LinkedIn: https://linkedin.com/in/123-jay-pan

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Source: https://flows.cv/jaypan
JSON Resume: https://flows.cv/jaypan/resume.json
Last updated: 2026-04-12