ML/Data Engineer

Quick Facts

• Role: ML/Data Engineer

Description

You will build production data infrastructure and real-time/near-real-time ingestion for an AI-powered sports performance intelligence platform. The work includes designing scalable time-series and longitudinal data architectures, graph data models, and low-latency feature pipelines for ML and AI systems. You’ll also implement evaluation, reliability checks, tenant-level data isolation, and end-to-end data provenance/observability.

Responsibilities

• Design and build production-grade data infrastructure for high-volume athlete performance and sensor data

• Build and operate real-time and near-real-time streaming ingestion systems

• Create storage and data architectures for high-volume time-series and longitudinal athlete records

• Provide low-latency derived metrics and production features to ML systems and AI agents

• Design and implement graph data models and schemas for athletes, training loads, injuries, performance, and outcomes

• Build data and ML evaluation infrastructure to assess model reliability, calibration, and performance in real cases

• Maintain tenant-level data isolation across clubs and customers

• Establish data provenance, lineage, auditability, and observability across the platform

• Partner with ML/AI engineers to deliver reliable data foundations for training, inference, and evaluation

• Work with sport scientists and domain experts to translate requirements into production systems

• Make pragmatic architecture decisions as the platform evolves

Requirements

• 5+ years full-time professional software or data engineering experience (excluding internships, university placements, coursework, academic projects)

• Proven experience building and operating production data infrastructure at scale

• Strong time-series data/database experience (InfluxDB, TimescaleDB, Prometheus, ClickHouse, or equivalent)

• Significant real-time/streaming ingestion experience (Kafka, Kinesis, Flink, Spark Streaming, Pulsar, or equivalent)

• Experience designing graph data models/schemas, not only querying

• Experience designing/operating multi-tenant systems with tenant-level isolation

• Strong Python and SQL

• Experience with production systems focused on reliability, scalability, observability, and data correctness

• Ability to own ambiguous technical problems and turn them into practical production architecture

• Experience working directly with data scientists, ML engineers, or other technical specialists

• English: Upper-Intermediate+ or above

Benefits

• Fully remote, office, or hybrid work options

• Mentoring and adaptation systems for new hires

• Professional and career growth support

• Bonus up to an additional 1,000 EUR/month based on expertise

• Access to a corporate training portal

• Private health insurance and sports compensation (depending on employment type)

• Certification compensation (e.g., AWS, PMP)

• Referral program