Scala Engineer

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 is not just another tech company. It is a space where our brilliant team consequently brings technology change to the business world and instead of following known paths, we are creating a new one - a next-generation, fully personalized, and AI-driven customer experience.

We successfully deliver an all-in-one tool - Synerise. An ever-evolving behavioral data platform, enhanced by AI to generate outstanding ROI in more than 30 markets for industry leaders in Retail, Banking, eCommerce, Automotive, Insurance, and Telco, processing more than 150 billion transactions annually. However, we don't limit ourselves solely to this solution. We’re building BaseModel - a foundation model for behavioral data embedded within a novel platform for enterprise Data Science teams, that is another step on our path to create innovation in AI and demonstrate its potential for the business world.

Having such great solutions, we are looking for a highly motivated Scala Engineer to join our brave and brilliant Synerise Team. See if you fit our diverse and dynamic environment, where we constantly evolve together with the growth of our clients.

Our Backend Team works with Scala, Kafka, ElasticSearch, ScyllaDB, Azure, GCP, and Kubernetes. Distributed tracing (OpenTelemetry, Jaeger), structured logging, and rich metrics are core to how we build and operate services - not an afterthought. Both Backend and Frontend teams are supported by dedicated QA and Infrastructure teams. We like to experiment, and we have the environment to do it. We use AI coding assistants (Claude, Copilot) as a daily part of our workflow - not to replace engineering judgment, but to move faster on the parts that benefit from it.

The platform handles up to 28k API requests per second at peak with p95 latency under 100ms. You'd be joining the team responsible forevent processing and analytics - the part of the platform that ingests, processes, and stores customer behavior events at scale, and powers the analytical capabilities our clients rely on. It's the data backbone the rest of the product is built on.

You'll be working on software that:

• Ingests and processes high-volume streams of customer behavior events from online and offline commerce - peaks at 30k events per second.

• Reliably stores and organizes event data for downstream analytics and querying.

• Powers the analytics module that turns raw events into insights for our clients.

• Has to stay reliable, ordered, and correct under load.

• Runs on a microservices architecture on Kubernetes.

What will you do on a daily basis?

• Design architecture and patterns for processing and storing high-volume datasets.

• Build software with a strong focus on performance and optimization.

• Translate complex functional and technical requirements into detailed designs.

• Analyze large data stores and surface meaningful insights.

What will make us a perfect match?

• Strong Scala skills, with production experience using an effect system (Cats Effect, ZIO, or Akka/Pekko) - we care more about depth of understanding than years on your CV.

• Experience designing or significantly contributing to event-driven systems in production - comfortable reasoning about ordering guarantees, idempotency, replay, and failure modes.

• Strong working knowledge of Apache Kafka - not just as a consumer API, but understanding partitioning, delivery guarantees, consumer groups, rebalance issues, and consumer lag.

• Hands-on Kubernetes skills sufficient to independently diagnose pod issues, resource limits, HPA behavior, and networking.

• Experience with both SQL and NoSQL databases, with a clear sense of when each is the right tool.

• Ability to design for scale and growth - partitioning, sharding, tenant isolation, backpressure.

• Strong debugging and troubleshooting skills in distributed environments.

What will convince us even more?

• Solid grasp of JVM internals - GC behavior, profiling, heap and thread dump analysis.

• Experience with observability: distributed tracing (OpenTelemetry, Jaeger), structured logging, metrics - for us this is core to daily work.

• Experience with microservices architecture, including awareness of common pitfalls (distributed transactions, eventual consistency, contract versioning).

• Experience working in a cloud environment (Azure / GCP) - our stack runs primarily on AKS.

What can we provide for you?

• Work on a production-grade, large-scale system used by enterprise customers worldwide.

• Real technical challenges around performance, data volume, and distributed systems.

• Opportunities for continuous technical growth and deeper ownership over system design.

• Support from experienced engineers and a strong engineering culture.

• Influence on architectural and technical decisions within your team.