Scala Engineer

Technologies we use

Expected

• Scala
• Apache Kafka
• Kubernetes
• SQL

Optional

• Claude Code
• Cursor
• GitHub Copilot
• Microsoft Azure
• Google Cloud Platform
• Java
• Rust
• Python

Operating system

• Windows
• macOS

About the project

We successfully deliver an all-in-one tool - Synerise (YouTube link). 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 (website link) - 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 Software 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 primarily with Scala, Apache Kafka, ElasticSearch, ScyllaDB, Kubernetes, and cloud environments (Azure and GCP). Observability is a first-class concern: distributed tracing (OpenTelemetry, Jaeger), structured logging, and rich metrics are embedded into how we design and operate services.

The platform handles up to 28k API requests per second at peak, maintaining p95 latency below 100 ms.

As part of the event processing and analytics team, you will work on systems that:

• Ingest and process high volume streams of customer behavior events from online and offline sources (peaks of 30k events per second)

• Reliably store and organize event data for analytics and querying at scale

• Power analytical modules transforming raw events into actionable insights

• Must remain ordered, consistent, and reliable under heavy load

• Run in a distributed microservices architecture on Kubernetes

We like to experiment and continuously improve. Backend and Frontend teams are supported by dedicated QA and Infrastructure teams, and AI coding assistants (such as Copilot and Claude) are part of our daily workflow — helping us move faster where it makes sense, without replacing engineering judgment.

This is how we organize our work

This is how we work

• in house

Your responsibilities

• Design architectures and patterns for processing and storing large-scale, high‑volume event data.
• Build backend software with a strong focus on performance, scalability, and reliability.
• Translate complex functional and technical requirements into robust system designs.
• Work with large datasets to ensure data correctness and meaningful analytics outcomes.
• Diagnose and troubleshoot issues in distributed, event-driven systems.
• Collaborate closely with other backend engineers, QA, and Infrastructure teams.

Our requirements

• Strong Scala skills with production experience using an effect system (Cats Effect, ZIO, Akka/Pekko).
• Hands-on experience designing or contributing to event-driven systems in production.
• Deep understanding of Apache Kafka, including partitioning, delivery guarantees, consumer groups, rebalancing, and lag.
• Practical knowledge of Kubernetes, sufficient to diagnose pod issues, resource limits, autoscaling, and networking.
• Experience working with both SQL and NoSQL databases, with a clear understanding of their trade-offs.
• Ability to design systems for scale: partitioning, sharding, backpressure, and tenant isolation.
• Strong debugging and troubleshooting skills in distributed environments.
• Hands-on experience using AI coding (e.g., Claude Code, Cursor, GitHub Copilot) as part of daily software engineering work.

Optional

• Solid understanding of JVM internals (GC, profiling, heap and thread dumps).
• Production experience with observability: distributed tracing, structured logs, and metrics.
• Experience designing and operating microservices architectures, with awareness of common pitfalls.
• Hands-on experience working in cloud environments (Azure and/or GCP), preferably with AKS.
• Working knowledge of Java or Rust.
• Experience with ETL pipelines, Python, or shell scripting.
• Contributions to open-source projects in the Scala or JVM ecosystem.
• Familiarity with event sourcing and CQRS.

What we offer

• 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.

This is how we work on a project

• Clean Code
• code review
• event modeling
• architect / technical leader support
• DevOps
• team-level deployment
• active monitoring
• NFR

Benefits

• sharing the costs of sports activities
• private medical care
• remote work opportunities
• flexible working time
• fruits
• no dress code

Synerise S.A.

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 (more info: Synerise Explainer Video (youtube.com): an ever-evolving 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 the Base Model platform (more info BaseModel.AI | Apply science to behavioral data. Automatically.) - 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.