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.

Since 2006, our solutions have helped more than 5,000 teams around the world make better decisions about athlete health, readiness, performance, and game-day strategy. Our technology is used across the NFL, NBA, NHL, MLS, EPL, AFL, NRL, NCAA, and many other elite sporting organisations.

We are now building the AI layer that brings together the depth of data Catapult has collected over two decades. Our goal is to become an intelligence partner for coaches, athletes, and performance staff, connecting data from sensors, video, sport science, and historical performance to surface insights that practitioners can trust.

We are looking for a Senior ML / Data Engineer to build the data infrastructure that powers this next generation of performance intelligence.

This is a senior production engineering role. You will own significant parts of the architecture that ingest, store, transform, serve, and evaluate athlete performance data. The systems you build will support real-time and machine learning use cases across a global, multi-tenant platform.

You will work closely with data scientists, ML engineers, software engineers, and sports scientists to turn complex performance requirements into reliable, scalable production infrastructure.

This role is suited to an engineer who has spent several years building and operating production systems and is comfortable taking ownership of architecture and technical decisions.

What You'll Do:

• Design and build production data infrastructure for high-volume athlete performance and sensor data.
• Build and operate real-time and near-real-time ingestion systems for streaming data.
• Design storage and data architectures for high-volume time-series data and longitudinal athlete records.
• Build infrastructure that makes production features and derived metrics available to machine learning systems and AI agents with low latency.
• Design and implement graph data models and schemas representing relationships between athletes, training loads, injuries, performance, and outcomes.
• Build data and ML evaluation infrastructure that helps measure model reliability, calibration, and performance across real-world cases.
• Design systems that maintain strong tenant-level data isolation across clubs and customers.
• Establish appropriate data provenance, lineage, auditability, and observability across the platform.
• Work with ML and AI engineers to provide reliable data foundations for model training, inference, and evaluation.