Manager, Software Engineering

Leads the engineering team behind the Athlete Data Platform — the services, schemas and data systems that give every Nike athlete a single, consent-aware record of who they are and what they are training for, plus the intelligence layer that reads from it to power personalisation across Nike Run Club (NRC), Nike Training Club (NTC) and the sport-science research surfaces behind them. The post-holder manages a full-stack squad spanning backend services, mobile SDK integration and the data pipelines that keep the athlete record trustworthy end-to-end, and is measured on how reliably that foundation unlocks the surfaces that depend on it. The role reports to the Director of Engineering and partners daily with Principal and Lead Engineers, Sport Science, Data Science, Product, Design, the NRC and NTC app teams, Privacy and InfoSec, and the Platform Engineering teams the services depend on.

• 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 used day-to-day — and instrument how fast those practices actually move delivery
• With the honesty to swap out what is not 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-versus-buy calls for athlete data capabilities, identifying 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 SDK contracts, client-cache posture and offline/online reconciliation behaviour that keeps the athlete record consistent across apps and devices.
• Partner with Sport Science and Data Science to productionise models grounded in Nike Sport Research Lab (NSRL) biomechanics, fuelling 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 and biometric — safe, consent-aware and compliant end-to-end.
• Represent Athlete Data Platform in cross-MAC architecture reviews, platform-readiness sequencing and executive forums, translating between engineering, science and business audiences.
• Grow the technical bar on the team through structured feedback, design-review coaching and mentorship across levels.

• Demonstrated fluency in AI-Native engineering, with specific, recent examples of how state-of-the-art AI tooling and practices accelerated real delivery on teams led
• And 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 versus what only 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 organisations not directly managed.
• 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, with comfort in 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 Director and VP level can decide against.
• Strong written and verbal communication across engineering, science, product and executive audiences.

• Has shipped platform services at consumer scale, holds clear opinions on composable architecture and build-versus-buy, and can walk a hiring panel through the specific tools, loops and metrics used to compress weeks of work into days.
• 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 they were found.
• A team-first orientation — scaling oneself by making the engineers around you better and the systems around you simpler.
• Curiosity about the athlete and the science behind the data the team moves.
• Clear-eyed judgment on AI tooling — excitement about the leverage, honesty about what actually works.

It looks for athletes who can push boundaries, elevate its potential and continue leading it to greatness.