Senior Data Platform Engineer
About The Position
We are looking for a Senior Data Platform Engineer to join the team that builds and operates Kambi's data and model-serving capabilities behind our sportsbook platform. This platform powers automated decision-making across the business and directly influences risk management, behavior modeling, and operational insights. You will work at the intersection of distributed systems, near real-time data processing, model serving, and cloud-native infrastructure - enabling ML-driven features that update in milliseconds. You'll join a skilled, self-organized, and friendly engineering team working on data-intensive systems that support near real-time decision-making across millions of events. You'll collaborate closely with other backend engineers, data engineers, and quant specialists to: Build and evolve data pipelines and model-serving flows that support near real-time risk-related signals. Develop and maintain cloud-native backend services in AWS (K8s, Lambda, DynamoDB, MemoryDB). Work with streaming and event-driven systems (Kafka-based pipelines). Contribute to the lifecycle of operational ML models (feature generation, model execution, monitoring, deployments). Implement model serving workflows running on Java/Python microservices, AWS services, or scalable batch frameworks. Build resilient, observable infrastructure using Terraform, logging, metrics, tracing, and automated alerting. Shape how Kambi runs data and ML-powered systems at scale. Own services end-to-end, including deployments, performance tuning, incident handling, and on-call participation. This is a team environment, so problems are solved together, not in isolation.
Requirements
- Strong experience with Java (Spring Boot, microservices, distributed systems).
- Good working knowledge of Python for data processing or ML integration.
- Hands-on experience with Kafka or similar event-streaming technologies.
- Solid understanding of distributed architectures, concurrency, and high-throughput systems.
- Experience deploying to cloud environments (AWS preferred).
- Familiarity with SQL/NoSQL data stores and caching systems (Redis/MemoryDB/ElastiCache).
- Comfort with CI/CD, containers, Kubernetes, and Terraform as part of everyday work.
Nice To Haves
- Experience supporting production ML pipelines, feature stores, or real-time scoring.
- Experience with Spark, Airflow, dbt, or similar tools in data transformation workflows.
- Knowledge of risk modeling, behavioral modeling, or real-time decision systems.
- Strong debugging skills in distributed, asynchronous, event-driven systems.
Responsibilities
- Build and evolve data pipelines and model-serving flows that support near real-time risk-related signals.
- Develop and maintain cloud-native backend services in AWS (K8s, Lambda, DynamoDB, MemoryDB).
- Work with streaming and event-driven systems (Kafka-based pipelines).
- Contribute to the lifecycle of operational ML models (feature generation, model execution, monitoring, deployments).
- Implement model serving workflows running on Java/Python microservices, AWS services, or scalable batch frameworks.
- Build resilient, observable infrastructure using Terraform, logging, metrics, tracing, and automated alerting.
- Shape how Kambi runs data and ML-powered systems at scale.
- Own services end-to-end, including deployments, performance tuning, incident handling, and on-call participation.
Benefits
- A skilled, self-organized, and fun engineering team that supports one another
- A chance to shape the next generation of Kambi's data and ML infrastructure
- You get to work on complex, meaningful engineering problems
- Strong engineering culture, autonomy, and an environment that values quality
- Hybrid work, in-person collaboration, and a highly international company
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
Industry
Publishing Industries
Education Level
No Education Listed
Number of Employees
1,001-5,000 employees
