Senior ML/Data Engineer

Catapult is building the future of sports performance technology, with a mission to Unleash the Potential of every athlete and team on earth. We don't just work in the sporting industry; we are actively changing it. Since 2006, our solutions have been leading the way in sports performance software, science, and data, in a world where 1% can literally mean the difference between winning and losing. We work with over 5,000+ teams around the world, empowering coaches, managers and trainers in premier teams in the NFL, NBA, NHL, MLS, EPL, AFL, NRL, NCAA and more. We provide the information they need to optimize athletes' health, game-day readiness, and performance, as well as in-game tactics. Catapult is a sports technology company that empowers professional teams to make data-driven decisions. We deliver health, performance, video, and AI insights from the locker room to competitive environments, ensuring every decision is an opportunity to gain an advantage, sharpen performance, and build lasting success. WE WANT PEOPLE WHO ARE PASSIONATE ABOUT MACHINE LEARNING Catapult Sports is the global leader in athlete performance technology, trusted by elite clubs and national programs across every major sport on every continent. Our hardware and software platforms are on the training ground and in the arena with the best teams in the world - and we have been there, continuously, for over two decades. We are now building the AI layer that compounds everything Catapult has ever measured. The goal is ambitious: to become the indispensable intelligence partner for every coach and athlete in every sport - a system that connects the full depth of performance data and surfaces the right insight at the right moment, with the confidence to act on it. This role is the data foundation of that platform. You will own the infrastructure every AI agent reasons over: the data architecture, the feature store, the sport knowledge graph, and the evaluation framework that makes every recommendation trustworthy before it reaches a practitioner. This is not a support role or a pipeline maintenance job. It is the highest-leverage engineering position in Phase 1 of a platform that will define what performance intelligence means in professional sport. If you have built data infrastructure at scale - time-series, feature serving, graph - and you care deeply about whether the systems you build are actually trustworthy, not just functional, this is the role that will define your next chapter. WHAT YOU'LL DO Develop and execute strategic workforce plans, coaching senior leaders on organisational design, change management, communication, and talent development to drive engagement, retention, and business performance. Shape culture and values by identifying organisational needs and implementing impactful, scalable people solutions. Drive a consistent, high-quality employee experience across the EMEA region, spanning 3 offices, 16 countries, and multiple employment frameworks. Provide hands-on support and strategic partnership to leaders, teams, and employees, ensuring people strategies are aligned with business goals. Partner with the Talent team to deliver strategic initiatives across learning and development, diversity, equity and inclusion, reward, workforce planning, and talent acquisition. Mentor and lead regional P&C team members, fostering a high-performance culture. Leverage data and insights to identify trends, inform decision-making, and influence strategic outcomes. WHAT YOU'LL NEED NON-NEGOTIABLE 5+ years of production data engineering at scale, time-series databases, data lakes, feature stores in a real-time or near-real-time environment Experience building probabilistic evaluation frameworks or model calibration infrastructure, you understand the difference between a model that works and a model that is trustworthy Strong Python, Golang and SQL; experience with streaming ingestion (not batch-only) for live sensor or IoT data Graph database experience: you have designed schemas for complex relationship networks, not just queried existing ones Production experience with tenant-level data isolation at the infrastructure level, not just access control Comfort working directly with domain scientists and AI engineers; you translate data requirements into durable infrastructure, not just pipelines STRONGLY PREFERRED Experience with causal inference or counterfactual modeling over graph structures Background in sports technology, wearable sensor data, or biomechanics data, you understand what makes athlete time-series data structurally different from standard telemetry Experience building knowledge graphs with custom ontologies for a specific domain Familiarity with LLM evaluation frameworks and their limitations in probabilistic sport science contexts Experience working with AWS (ECS, EC2, Lambda, SNS, SQS, etc), GraphQL, REST, gRPC, Postgres, Mongo THE STACK No vendor names are locked, the capabilities are #J-18808-Ljbffr PVH (Tommy Hilfiger/Calvin Klein)