We’re looking for a hands-on QA Automation Engineer to ensure the quality and reliability of our full-stack web platform. The team currently has no dedicated QA function, so this role comes with real ownership and influence – you’ll help shape automation practices, test coverage, and overall quality standards from the ground up.

This is not a narrow, UI-only QA role. You’ll work across frontend, APIs, backend services, data workflows, file-based integrations, and cloud-based components, collaborating closely with developers and other stakeholders throughout the delivery lifecycle.

Your work will directly impact system stability, release predictability and engineering maturity, helping the team deliver reliable, production-grade systems.

What you’ll actually work on

• Design, develop, and maintain automated E2E, integration and regression tests across UI and API layers.

• Validate frontend–backend interactions, including data integrity, state changes and API contract compliance.

• Test backend services, background jobs and asynchronous workflows, ensuring correct logic and error handling.

• Perform API testing covering request/response schemas, edge cases and failure scenarios.

• Validate data correctness using SQL across relational and analytical databases, including schema changes and migrations.

• Test file ingestion and SFTP-based workflows, ensuring correct handling of malformed or incomplete inputs.

• Support quality validation of cloud-based workflows (AWS services) through functional testing and basic log analysis.

• Maintain structured test plans, test cases and regression suites, ensuring coverage across all environments.

• Collaborate closely with developers and stakeholders, contributing to acceptance criteria, defect tracking, and continuous quality improvements.

Daily work reality

• You’ll work on a backend-heavy, data-driven platform where understanding system behaviour is as important as testing it.

• Reading code, analysing logs and identifying root causes across systems is part of the job.

• Quality is a shared responsibility, but you will be the driver of QA automation and testing practices.

• You’ll help build testing foundations rather than inherit an already mature QA setup.
  

What we expect from you

• Solid experience in QA automation with a good understanding of the testing lifecycle.

• Ability to independently design and maintain automated tests across UI and API layers.

• Experience testing backend services and APIs, including error handling and edge cases.

• Strong Python skills for test automation.

• Good SQL knowledge for validating data correctness.

• Experience working with CI/CD pipelines where automated tests are part of the release process.

• Familiarity with cloud-based systems (AWS or similar) and basic log analysis.

• Analytical mindset, attention to detail, and a clear ownership approach to quality.

• Clear communication and ability to work closely with developers and stakeholders.

What we offer:

• Monthly salary: €3200 – €4500 gross, depending on experience and scope.

• Participation in the company’s stock options program.

• Flexible benefits and a personal learning budget.

• 10 Growth Days per year dedicated to learning and development.

• Real ownership and autonomy in your role.

• Hybrid work model, with flexibility and a preference for at least one day per week in the office.

• Support from experienced colleagues across data, analytics and AI.

• A strong, international team that values openness and collaboration.

More about us:

Exacaster builds production-grade data, analytics, and applied AI systems for large-scale, real-world environments, helping our clients impact the daily lives of over 40 million consumers across the Baltics, the USA, Central and South America, and the Caribbean.

We work across three core areas: Customer Value Management (CVM), Data Management, and AI Accelerators. Our CVM platforms and services support telecom clients serving 20+ million subscribers across 16 countries, alongside data, BI, and analytics projects in other industries. We also build applied AI and ML solutions, including GenAI use cases and MLOps / AIOps capabilities, focused on production rather than experimentation. Across all work, we deal with high-volume, imperfect data and design systems used for real decision-making.