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ML/Data Engineer

Andersen Zobacz wszystkie oferty

Warszawa

Poznań

Wrocław

Gdańsk

Wygasa 29 lis 2026

3 dni temu

W skrócie

Andersen seeks an ML/Data Engineer for an AI sports platform. Responsibilities include designing data infrastructure, real-time ingestion, graph data models, and ML evaluation systems. Requires 5+ years of professional data engineering experience, time-series/streaming data expertise, and graph data modeling.

Słowa kluczowe

data engineering machine learning ai streaming data time-series database graph database kafka influxdb

Skrót przygotowany przez AI na podstawie treści ogłoszenia.

Andersen is hiring an ML/Data Engineer for a project developing an AI-powered sports performance platform and delivering scalable data solutions and real-time insights for athletes and coaches.

Our customer is a global technology provider specializing in performance monitoring, data analytics, and operational optimization. It develops software platforms and data-driven solutions that help organizations improve efficiency, gain valuable insights, and make informed decisions. Serving clients across international markets, the company focuses on innovation and integrated technologies that enhance performance and support long-term growth.

The project is focused on developing an AI-powered sports performance intelligence platform that integrates sensor data, video, sports science, and historical athlete performance data. It includes building scalable data infrastructure and machine learning capabilities to deliver reliable, real-time insights for coaches, athletes, and performance teams across a global platform.

Responsibilities:

Designing and building production data infrastructure for high-volume athlete performance and sensor data.
Building and operating real-time and near-real-time ingestion systems for streaming data.
Designing storage and data architectures for high-volume time-series data and longitudinal athlete records.
Building infrastructure that makes production features and derived metrics available to machine learning systems and AI agents with low latency.
Designing and implementing graph data models and schemas representing relationships between athletes, training loads, injuries, performance, and outcomes.
Building data and ML evaluation infrastructure that helps measure model reliability, calibration, and performance across real-world cases.
Designing systems that maintain strong tenant-level data isolation across clubs and customers.
Establishing appropriate data provenance, lineage, auditability, and observability across the platform.
Working with ML and AI engineers to provide reliable data foundations for model training, inference, and evaluation.
Working with sport scientists and domain experts to translate complex requirements into durable production systems.
Making pragmatic technology and architecture decisions as the platform evolves.

Must-haves:

Full-time professional software or data engineering for 5+ years, 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 (InfluxDB, TimescaleDB, Prometheus, ClickHouse, or equivalent).
Significant experience with real-time or streaming data ingestion (Kafka, Kinesis, Flink, Spark Streaming, Pulsar, or equivalent).
Experience designing graph data models or graph database schemas, not only querying an existing graph database.
Experience designing or operating multi-tenant systems with tenant-level data isolation.
Strong Python and SQL.
Experience with production systems where reliability, scalability, observability, and data correctness matter.
Ability to own ambiguous technical problems and turn them into practical production architecture.
Experience working directly with data scientists, ML engineers, or other technical domain specialists.
Level of English – from Upper-Intermediate+ and above.

Nice-to-haves:

Professional experience with Go.
Experience building probabilistic evaluation, model calibration, or model monitoring infrastructure.
Experience with causal inference, counterfactual modelling, or simulation.
Understanding of wearable sensors, IoT data, biomechanics, sports technology, or other high-frequency telemetry.
Knowledge graphs or domain-specific ontologies.
LLM or AI evaluation frameworks and an understanding of their limitations.
Experience with AWS, including ECS, EC2, Lambda, SNS, or SQS.
Experience with GraphQL, REST, gRPC, Postgres, MongoDB, or similar technologies.

Reasons why this job would be interesting to you:

Experience in teamwork with leaders in FinTech, Healthcare, Retail, Telecom, and others. Andersen cooperates with such businesses as Samsung, Siemens, Johnson & Johnson, BNP Paribas, Ryanair, Mercedes, TUI, Verivox, Allianz, T-Systems, etc..
The opportunity to change the project and/or develop expertise in an interesting business domain.
Job conditions – you can work both fully remotely and from the office or can choose a hybrid variant.
Guarantee of professional, financial, and career growth! The company has introduced systems of mentoring and adaptation for each new employee.
The opportunity to earn up to an additional 1,000 EUR per month, depending on the level of expertise, which will be included in the annual bonus, by participating in the company's activities.
Access to the corporate training portal, where the entire knowledge base of the company is collected and which is constantly updated.
Bright corporate life (parties / pizza days / Play

Station / fruits / coffee / snacks / movies).