Demonstrated fluency in AI-Native engineering - specific, recent examples of how state-of-the-art AI tooling and practices accelerated real delivery on teams you've led, with the architectural literacy to distinguish and apply the primitives: agents (the LLM decision loop) and multi-turn conversations (stateful, context-managed sessions); Model Context Protocol (MCP) integrations that expose Nike systems to those agents; workflow orchestration that chains agent steps deterministically; skills as composable guides (soft, loadable instruction packs) and hooks as deterministic gates (hard enforcement points in the dev loop); spec-driven development and evaluation harnesses that keep the whole thing honest - and clear-eyed judgment on what actually works vs. what just looks like it does
5+ years in technology and software engineering roles, including 3+ years in people management or team leadership; Bachelor's degree in Computer Science, Engineering, or a related field, or an equivalent combination of education, experience, and training
Experience leading engineering teams in a highly matrixed environment, with the ability to drive outcomes across partner organizations you don't directly manage
Track record leading full-stack squads shipping consumer-scale platforms - backend services, mobile SDK integration, and data pipelines - with ownership of availability, latency, and cost
Hands-on technical grounding in modern backend stacks (Go, Kotlin/Java, or Python; ConnectRPC/gRPC/REST; event-driven pipelines on Kafka/Kinesis/SNS/SQS; cloud-native deployment on AWS or equivalent)
Working familiarity with at least one mobile platform (iOS/Swift or Android/Kotlin) sufficient to lead technical reviews across backend ↔ client boundaries
Experience partnering with Data Science, ML, or Sport Science teams to move models from notebook to production: data contracts, feature pipelines, evaluation, and safe rollout
Working familiarity with the shape of activity and health data (GPS traces, sensor time series, HR/HRV, workout aggregates, training load) and the consent, privacy, and compliance expectations that come with them
Strong track record hiring, growing, and performance-managing engineers across levels; comfort with difficult feedback conversations and succession planning
Proven ability to lead technical design reviews, drive cross-squad alignment on service contracts and data models, and author written proposals that stakeholders at the Director and VP level can decide against
Strong written and verbal communication across engineering, science, product, and executive audiences
WHAT YOU WILL WORK ON
You'll lead the engineering team behind the Athlete Data Platform - the record, the intelligence layer, and the pipelines that keep both trustworthy - and you'll be measured on how reliably that foundation unlocks the surfaces that depend on it. Your team's work is what makes the athlete-facing apps feel like they know the athlete: the plan that adapts actually adapts, the race recommendation is actually right, and the data NRC wrote is the data NTC reads.
Build and operate the Athlete Data Platform team - hire, onboard, grow, and performance-manage a full-stack squad of senior and mid-level engineers plus a Lead or Principal IC partner
Set the AI-Native engineering bar for the team - the agents, workflow orchestrations, MCP integrations, skills (as composable guides), hooks (as deterministic gates), spec-driven practices, and evaluation harnesses the squad uses day-to-day - and instrument how fast those practices actually move delivery, with the honesty to swap out what isn't working
Partner with Principal Engineers, Product, Sport Science, and Data Science to set the quarterly roadmap and translate platform strategy into committed deliverables, with written specs reviewed before implementation starts
Own the technical and operational health of the platform: service architecture, data-model quality, test coverage, latency, availability, cost, on-call load, and the consumer SLAs the NRC and NTC squads depend on
Make and defend build-vs-buy calls for athlete data capabilities - where Nike's differentiation requires in-house platform ownership, and where a vendor integration is the right move
Partner with the Activity Apps (NRC/NTC) iOS and Android squads on the SDK contracts, client-cache posture, and offline/online reconciliation behavior that keeps the athlete record consistent across apps and devices
Partner with Sport Science and Data Science to productionize models grounded in Nike Sport Research Lab (NSRL) biomechanics, fueling, and recovery research - data contracts, feature pipelines, evaluation harnesses, consent-aware inference, and safe progressive rollout
Partner with Security, Privacy, and Data Governance to keep athlete data - location, health, biometric - safe, consent-aware, and compliant end-to-end
Represent Athlete Data Platform in cross-MAC architecture reviews, platform-readiness sequencing, and executive forums; translate between engineering, science, and business audiences
Grow the technical bar on the team through structured feedback, design-review coaching, and mentorship across levels
WHAT YOU BRING
A bias to action balanced with the discipline to write a spec before writing code
Deep pride in craft, and a habit of leaving teams and systems stronger than you found them
A team-first orientation - you scale yourself by making the engineers around you better and the systems around you simpler
Curiosity about the athlete and the science behind the data your team moves
Clear-eyed judgment on AI tooling - excitement about the leverage, honesty about what actually works
We offer a number of accommodations to complete our interview process including screen readers, sign language interpreters, accessible and single location for in-person interviews, closed captioning, and other reasonable modifications as needed. If you discover, as you navigate our application process, that you need assistance or an accommodation due to a disability, please complete the Candidate Accommodation Request Form.
