Senior QA Engineer, IMF

We are looking for a dedicated QA Engineer to join our IMF team and take ownership of quality across our in-memory database (IMF) and its surrounding services. IMF is a complex, distributed system built on Kubernetes and Google Cloud Platform that stores user events and profiles and performs real-time analyses with very low latency. It spans multiple technology stacks (C++, Go, Python), communicates over gRPC and Kafka, and runs in a high-availability configuration across tens of instances.

You will be the first QA engineer embedded in this team, giving you the opportunity to shape the QA culture, processes, and tooling from the ground up. You won't be doing this alone — you'll have the support of the team, other embedded QA engineers across the organization, and a central QA chapter to exchange ideas and best practices with. Your goal is to increase the speed and confidence of development by ensuring that engineers have clear guidance on what to test, that regressions are caught early, and that we can safely validate changes at production scale. IMF powers real-time analytics for more than 1,400 global brands — the quality of this system directly impacts our customers.

The team works remotely in the Central European Time Zone. We are happy to meet you in Bratislava, Brno or Prague, where our headquarters are located.

Responsibilities

1. Define and own the QA strategy for the IMF ecosystem end-to-end — from data ingestion through storage and computation to the clients consuming the results.
2. Plan and execute QA for new features: create test plans during project kickoff, participate in backlog and tasks refinement and technical design discussions, and identify the right testing approach for each change.
3. Build and maintain automated test suites and tooling integrated into our CI/CD pipelines — including test data generation, automated comparison of expected vs. actual results, and test environment management — so that every merge request gets reliable, fast feedback.
4. Establish quality practices and processes across the team: define testing guidelines, set testing expectations in the Definition of Done, bring visibility into test coverage across components, track quality metrics, and ensure QA is involved from the start of feature work so that quality becomes a shared team responsibility.
5. Design and execute production-scale testing and support safe release strategies: validate latency, throughput, memory, and CPU behavior under realistic and peak traffic conditions, and leverage techniques such as shadow deployments, canary rollouts, and comparison tooling to validate changes with confidence before full rollout.
6. Collaborate within a multi-expertise team spanning C++, Go, and Python engineers as well as SRE/DevOps — and bring the quality perspective into technical design, code reviews, and deployment decisions.
7. Coordinate with central QA and other QA engineers across the organization — share best practices, align on tooling and frameworks, and participate in the global QA chapter.

Qualifications

Must-have

• Proven experience as a QA Engineer working with complex, distributed backend systems (not primarily UI/frontend testing).
• Ability to design a test strategy for a multi-component system and identify the right types and levels of testing for each area.
• Hands-on experience with test automation and integrating automated tests into CI/CD pipelines.
• Experience with scripting and tooling in Python, Go, or Shell for test automation, data generation, and validation.
• Strong communication and collaboration skills — you will work within a multi-expertise team spanning several technology stacks and need to drive quality practices proactively.
• Ability to work independently and take ownership — you will be the first QA engineer on the team and will shape the QA direction.

Nice-to-have

• Ability to reason about data correctness, consistency, and integrity in systems that process and store large volumes of data.
• Experience with performance and load testing — designing benchmarks, measuring latency/throughput, and identifying bottlenecks.
• Comfortable working with Kubernetes and cloud infrastructure (GCP or similar) — deploying and testing services in containerized environments.
• Experience with gRPC-based services and testing inter-service communication.
• Familiarity with Kafka and event-driven architectures — understanding message flows, ordering, and partitioning.
• Experience with safe release strategies: shadow traffic, canary deployments, feature flags, old-vs-new comparison.
• Familiarity with observability tools (Grafana, VictoriaMetrics, Sentry, or similar) for monitoring test results and system behavior.

Our Stack

• Languages: C++, Go, Python, Scala
• Messaging: Apache Kafka
• Communication: REST, gRPC, Protocol Buffers
• Infrastructure: Kubernetes (GKE), Google Cloud Platform
• Databases: MongoDB, Redis
• Monitoring: Prometheus, Influx, VictoriaMetrics, Grafana, Sentry
• CI/CD: GitLab

Your Success Story

First 30 Days

• Get to know the team, the product, and how IMF fits into the broader Bloomreach platform.
• Learn about the system architecture: data flow from ingestion through storage and computation to the clients consuming analytical results.
• Understand existing test coverage — what is tested, what is not, and where the biggest gaps and risks are.
• Start participating in team ceremonies (planning, syncs, grooming) and build relationships with engineers across all technology stacks.
• Connect with other QA engineers in the organization and the central QA chapter.

First 90 Days

• Deliver your first test plans for active projects, integrated into the team's grooming and development workflow.
• Establish initial automated test suites for the highest-risk areas and integrate them into CI/CD pipelines.
• Introduce QA practices into the team's development process — Definition of Done, MR checklists, test expectations, and coverage visibility.
• Begin building tooling for test data generation and result comparison.
• Participate in validating at least one significant feature or infrastructure change with structured testing.

First 180 Days

• Own a comprehensive QA strategy for the IMF ecosystem, covering automation, tooling, and production-scale testing approaches.
• Have robust automated test suites running as part of every release pipeline, giving the team confidence in their changes.
• Establish performance testing baselines and regular benchmarks that catch regressions in latency, throughput, and resource usage.
• Drive the adoption of safe release strategies (shadow deployments, canary rollouts, comparison tooling) for major changes.
• Have established yourself as a trusted quality partner within the team — engineers increasingly know what to test, how to test it, and have the tools and confidence to ship faster.

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