ML/Data Engineer

Fácil Aplicaciónnuevo

Eusa, Spain

9 days ago

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Role details

Contract type

Internship / Graduate position

Employment type

Full-time (> 32 hours)

Experience required

5 years minimum

Working hours

Shift work

Job source

www.adzuna.es

Tech stack

AI Evaluation Amazon Web Services Amazon Elastic Compute Cloud Databases Data Architecture Information Engineering Graph Database Python (Programming Language) PostgreSQL Machine Learning MongoDB Operational Databases

+17 more

Prometheus Standard Sql Data Streaming Large Language Models Agentic-AI Build Management Low Latency Influxdb Apache Flink Apache Kafka Graphql Spark Streaming Machine Learning Operations Vertica Restful APIs Amazon Simple Queue Service (SQS) Grpc

Job description

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.
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.

Requirements

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 working with time-series data or time-series databases, such as InfluxDB, TimescaleDB, Prometheus, ClickHouse, or equivalent technologies.
Significant experience with real-time or streaming data ingestion, using technologies such as Kafka, Kinesis, Flink, Spark Streaming, 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. Professional experience with Go is highly desirable.
Experience working 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.

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