Scala Engineer

Quick Facts

Role: Scala Engineer (Backend)
Focus area: Event processing and analytics at scale
Scale/targets: up to 28k API requests/second (peak), p95 latency < 100ms; peaks at 30k events/second

Description

You’ll join the team responsible for ingesting, processing, and storing customer behavior events at scale—providing the data backbone for client analytical capabilities. The platform runs as microservices on Kubernetes and must remain reliable, ordered, and correct under load.

Responsibilities

• 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.
• Ingest and process high-volume streams of customer behavior events from online and offline commerce (up to 30k events/sec).
• Reliably store and organize event data for downstream analytics and querying.
• Power the analytics module that turns raw events into insights for clients.
• Ensure the system stays reliable, ordered, and correct under load.
• Operate on a Kubernetes-based microservices architecture.

Requirements

• Strong Scala skills with production experience using an effect system (Cats Effect, ZIO, or Akka/Pekko).
• Experience designing or significantly contributing to event-driven systems in production (ordering guarantees, idempotency, replay, failure modes).
• Strong working knowledge of Apache Kafka (partitioning, delivery guarantees, consumer groups, rebalance issues, 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 judgment on 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.
• Solid grasp of JVM internals (GC behavior, profiling, heap and thread dump analysis).
• Experience with observability: distributed tracing (OpenTelemetry, Jaeger), structured logging, and metrics.
• Experience with microservices architecture, including pitfalls (distributed transactions, eventual consistency, contract versioning).
• Experience working in a cloud environment (Azure / GCP); platform runs primarily on AKS.

Benefits

• 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.