QA Principal

Position Description:

We are looking for a Principal QA Engineer to take ownership of a company-wide QA system and lead its continued development. You will dive into the current processes, leadership model, automation and tooling direction, quality controls, and improvement roadmap; challenge existing approaches where needed, and drive the next stage of maturity across engineering domains. We need a senior technical leader who sees Quality Engineering as an engineering system, connects business and technical risks with practical changes in the SDLC, test architecture, automation, environments, release quality, and metrics, and turns systemic problems into measurable and sustainable improvements. This is a senior individual contributor role working primarily through QA Technical Leads and Senior QA Engineers and partnering with Engineering Managers, Product, Architecture, and DevOps/SRE, while remaining hands-on in high-impact reviews, pilots, and critical investigations.

Responsibilities:

• Take ownership of the company-wide Quality Engineering strategy, operating model, and improvement roadmap; continue evolving them as products, risks, and organizational needs change.

• Assess Quality Engineering maturity across domains, identify systemic bottlenecks and inconsistencies, and prioritize the improvements with the highest company-wide impact.

• Define and evolve cross-domain standards and guardrails for test architecture, coverage, quality evidence, testability, observability, release readiness, and production validation.

• Drive continuous improvement of the end-to-end QA process and embed shift-left practices, quality controls, and risk-based gates across the SDLC together with Product, Engineering, Architecture, and DevOps/SRE.

• Own the strategic direction of the QA toolchain and automation architecture, and validate key technical decisions hands-on through code, prototypes, and architecture reviews across UI and backend automation. Python is preferred, while Go, Node.js, or another suitable stack is also relevant.

• Continuously benchmark market practices and emerging technologies, including AI; validate ideas through experiments and turn successful findings into company-wide transformation initiatives such as framework modernization, contract and performance testing, smarter test selection, and cloud-native test environments. Actively use AI in daily engineering work and advance its adoption across QA.

• Define scalable release-quality principles and provide technical recommendations for systemic, cross-team, or ambiguous quality risks.

• Lead cross-team investigations and root-cause analysis for critical incidents, unstable environments, systemic automation failures, and quality problems without a clear owner.

• Establish meaningful quality metrics and make trends, risks, technical debt, and improvement outcomes visible to engineering leadership.

• Develop QA Technical Leads as domain-level technical owners, strengthening their ability to translate company-wide direction into sustainable practices and decisions within their domains.

• Influence, align stakeholders, and translate business, regulatory, and operational risks into clear engineering priorities.

Requirements:

• 8+ years in Quality Assurance / Quality Engineering, including technical leadership across multiple teams or domains.

• Experience defining QA strategy, standards, or large-scale transformations with measurable results.

• Deep knowledge of Quality practices risk-based testing, coverage strategy, shift-left, quality gates, testability, release readiness, and production quality signals.

• Strong hands-on programming experience and the ability to design and contribute to scalable UI and backend automation. Python is preferred, but strong experience with Go, Node.js, or another relevant stack is welcome.

• Strong experience with distributed backend systems, REST APIs, event-driven workflows, third-party integrations

• Experience with CI/CD, Docker/Kubernetes, cloud-native or GitOps environments, and observability tools.

• Ability to define useful quality metrics, communicate technical risk, and influence senior stakeholders

• A research-driven mindset, practical experience evaluating new technologies, and confident, responsible, and active use of AI tools in everyday engineering work.

• Good spoken and written English and Russian.

Nice to have:

 

• Experience in fintech, crypto, payments, compliance, financial operations, or another regulated domain with complex integrations and high operational risk.

• Experience with Kafka, MQ, Debezium, ledger-like systems, eventual consistency, and event-driven architecture.

• Experience with contract, performance, load, resilience, or reliability testing.