Scala Engineer: High-Volume Event Processing & Analytics

Synerise SA; Kraków; Hybrid; PLN 180,000 - 280,000; Full time

Job summary

Synerise SA seeks a highly skilled Scala Engineer to join our Backend Team. You will work on event processing and analytics, ingesting and processing high-volume customer behavior events, and supporting scalable, data-driven insights for enterprise clients.

We leverage Scala, Kafka, ElasticSearch, ScyllaDB, Azure, GCP, and Kubernetes, with a focus on reliability, performance, and observability in a distributed environment.

Qualifications

• Strong Scala skills with production experience in functional ecosystems.
• Experience designing or contributing to event-driven systems in production.
• Solid knowledge of Apache Kafka including partitioning, delivery guarantees, consumer groups.
• Hands-on Kubernetes skills to diagnose pod issues and scale workloads.
• Experience with SQL and NoSQL databases and choosing the right tool.
• Ability to design for scale with partitioning, sharding, and tenant isolation.
• Strong debugging and troubleshooting in distributed environments.

Responsibilities

• Design architecture for processing and storing high-volume datasets.
• Build software with performance and optimization focus.
• Translate complex requirements into designs.
• Analyze large data stores to surface insights.
• Work in a microservices architecture on Kubernetes.

Skills

Scala proficiency

Event-driven design

Distributed systems

Kafka expertise

Kubernetes

SQL/NoSQL

Debugging distributed systems

Performance optimization

Tools

Kafka

ElasticSearch

ScyllaDB

Azure

GCP

Kubernetes

Job description

Synerise SA seeks a highly skilled Scala Engineer to join our Backend Team. You will work on event processing and analytics, ingesting and processing high-volume customer behavior events, and supporting scalable, data-driven insights for enterprise clients.

We leverage Scala, Kafka, ElasticSearch, ScyllaDB, Azure, GCP, and Kubernetes, with a focus on reliability, performance, and observability in a distributed environment.