ML/Data Engineer

• 5+ years of full-time professional software or data engineering experience, excluding internships, university placements, coursework, and academic projects.
• Proven experience designing, building, and operating production data infrastructure at scale.
• Strong experience with time-series data or time-series databases such as InfluxDB, TimescaleDB, Prometheus, or ClickHouse, or equivalent technologies.
• Significant experience with real-time or streaming data ingestion using technologies such as Kafka, Kinesis, Flink, Spark Streaming, or Pulsar, or equivalent.
• Experience designing graph data models or graph database schemas, not simply querying or consuming an existing graph database.
• Experience designing or operating multi-tenant systems with tenant-level data isolation.
• Strong Python and SQL skills.
• Experience with production systems where reliability, scalability, observability, and data correctness matter.
• Ability to take ownership of ambiguous technical problems and turn them into practical production architectures.
• Experience working directly with data scientists, ML engineers, or other technical domain specialists.
• Experience building probabilistic evaluation, model calibration, or model monitoring infrastructure.
• Experience with causal inference, counterfactual modelling, or simulation.
• Experience working with wearable sensors, IoT data, biomechanics, sports technology, or other high-frequency telemetry.
• Experience building knowledge graphs or domain-specific ontologies.
• Experience with LLM or AI evaluation frameworks and an understanding of their limitations.
• Experience with AWS, including ECS, EC2, Lambda, SNS, SQS, or related services.
• Experience with GraphQL, REST, gRPC, Postgres, MongoDB, or similar technologies.
• English proficiency at B2+.

Nice to have:

• Professional experience with Go is highly desirable.

Project Responsibilities

• 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 to measure model reliability, calibration, and performance across real-world cases.
• Design systems that maintain strong tenant-level data isolation across clubs and customers.
• Establish 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.
• Work with sport scientists and domain experts to translate complex requirements into durable production systems.
• Make pragmatic technology and architecture decisions as the platform evolves.

• Candidates must be located in the EU.
• Working hours are 11:00–20:00 CET.