Senior ML/Data Engineer

About this role

<p>Catapult is building the future of sports performance technology, with a mission to Unleash the Potential of every athlete and team on earth.</p> <p>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.</p> <p>&nbsp;</p> <p>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.</p> <p>We are looking for a Senior ML / Data Engineer to build the data infrastructure that powers this next generation of performance intelligence.</p> <p>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.</p> <p>You will work closely with data scientists, ML engineers, software engineers, and sports scientists to turn complex performance requirements into reliable, scalable production infrastructure.</p> <p>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.</p> <p>&nbsp;</p> <p><strong>What You'll Do:</strong></p> <ul> <li>Design and build production data infrastructure for high-volume athlete performance and sensor data.</li> <li>Build and operate real-time and near-real-time ingestion systems for streaming data.</li> <li>Design storage and data architectures for high-volume time-series data and longitudinal athlete records.</li> <li>Build infrastructure that makes production features and derived metrics available to machine learning systems and AI agents with low latency.</li> <li>Design and implement graph data models and schemas representing relationships between athletes, training loads, injuries, performance, and outcomes.</li> <li>Build data and ML evaluation infrastructure that helps measure model reliability, calibration, and performance across real-world cases.</li> <li>Design systems that maintain strong tenant-level data isolation across clubs and customers.</li> <li>Establish appropriate data provenance, lineage, auditability, and observability across the platform.</li> <li>Work with ML and AI engineers to provide reliable data foundations for model training, inference, and evaluation.</li> <li>Work with sport scientists and domain experts to translate complex requirements into durable production systems.</li> <li>Make pragmatic technology and architecture decisions as the platform evolves.</li> </ul> <p>&nbsp;</p> <p><strong>What You'll Need:</strong></p> <ul> <li>5+ years of full-time professional software or data engineering experience, excluding internships, university placements, coursework, and academic projects.</li> <li>Proven experience designing, building, and operating production data infrastructure at scale.</li> <li>Strong experience working with time-series data or time-series databases, such as InfluxDB, TimescaleDB, Prometheus, ClickHouse, or equivalent technologies.</li> <li>Significant experience with real-time or streaming data ingestion, using technologies such as Kafka, Kinesis, Flink, Spark Streaming, Pulsar, or equivalent.</li> <li>Experience designing graph data models or graph database schemas, not simply querying or consuming an existing graph database.</li> <li>Experience designing or operating multi-tenant systems with tenant-level data isolation.</li> <li>Strong Python and SQL skills. Professional experience with Go is highly desirable.</li> <li>Experience working with production systems where reliability, scalability, observability, and data correctness matter.</li> <li>Ability to take ownership of ambiguous technical problems and turn them into practical production architectures.</li> <li>Experience working directly with data scientists, ML engineers, or other technical domain specialists.</li> <li>Experience building probabilistic evaluation, model calibration, or model monitoring infrastructure.</li> <li>Experience with causal inference, counterfactual modelling, or simulation.</li> <li>Experience working with wearable sensors, IoT data, biomechanics, sports technology, or other high-frequency telemetry.</li> <li>Experience building knowledge graphs or domain-specific ontologies.</li> <li>Experience with LLM or AI evaluation frameworks and an understanding of their limitations.</li> <li>Experience with AWS, including ECS, EC2, Lambda, SNS, SQS, or related services.</li> <li>Experience with GraphQL, REST, gRPC, Postgres, MongoDB, or similar technologies.</li> <li>What We Mean by Senior</li> </ul> <p>This role requires demonstrated professional ownership of production systems.</p> <p>We are not looking for someone whose primary exposure to these technologies comes from internships, university projects, coursework, or short-term placements.</p> <p>You do not need experience with every technology listed above. We care more about the depth of your production experience, your ability to design systems, and your track record of taking ownership of complex engineering problems.</p> <p>For example, strong experience designing and operating Kafka-based streaming infrastructure is more valuable to us than having used five different streaming technologies at a superficial level.</p> <p>Similarly, we are looking for engineers who have designed graph schemas, not simply listed Neo4j on their CV.</p> <p>&nbsp;</p> <p><strong>The platform requires capabilities including:</strong></p> <ul> <li>Real-time streaming i