Senior QA Engineer, IMF

Job Application for Senior QA Engineer, IMF at Bloomreach

Bloomreach is building the world’s premier agentic platform for personalization. We’re revolutionizing how businesses connect with their customers, building and deploying AI agents to personalize the entire customer journey.

We're taking autonomous search mainstream, making product discovery more intuitive and conversational for customers, and more profitable for businesses.

We’re making conversational shopping a reality, connecting every shopper with tailored guidance and product expertise — available on demand, at every touchpoint in their journey.

We're designing the future of autonomous marketing, taking the work out of workflows, and reclaiming the creative, strategic, and customer-first work marketers were always meant to do.

We're building all of that on the intelligence of a single AI engine — Loomi AI — so that personalization isn't only autonomous…it's also consistent. From retail to financial services, hospitality to gaming, businesses use Bloomreach to drive higher growth and lasting loyalty. We power personalization for more than 1,400 global brands, including American Eagle, Sonepar, and Pandora.

Responsibilities

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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 P