Junior Scala Engineer

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 Junior 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 for event 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?

• Develop architecture and design patterns to process and store high volume data sets.
• Develop software with a core focus around optimisation and performance.
• Translate complex functional and technical requirements into detailed design.
• Perform analysis of vast data stores and uncover 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.
• Working knowledge of Apache Kafka - not just as a consumer API, but understanding partitioning, delivery guarantees, consumer groups, rebalance issues, and consumer lag.
• Basic experience with at least one web framework and an understanding of how web applications work.
• Familiarity with SQL or NoSQL databases and basic database queries.
• Hands-on experience using AI coding (e.g., Claude Code, Cursor, GitHub Copilot) as part of daily software engineering work.
• Decent troubleshooting and analytical skills with a methodical approach to solving technical problems.
• A passion for building tools and learning new technologies.
• Strong analytical and communication skills.

What will convince us even more?

• Existing GitHub/Gitlab portfolio or personal projects.
• Experience with microservices architecture.
• Basic knowledge of AI/ML concepts.

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.