2x QA Engineer - Mid and Senior - Project: CaaS

Industry: education

Budget:

Mid: 127PLN/h + VAT

Senior: 170PLN/h + VAT

REMOTE FROM POLAND

Introduction & Summary

We are seeking two QA Engineers, at mid and senior levels, to join our team working on the CaaS project. This role is crucial in validating data and APIs within a cloud-based workflow environment, emphasizing an automation-first mindset. Candidates should possess hands-on experience in automated testing with Python and familiarity with AWS services, API testing, and data validation techniques.

Main Responsibilities

The QA Engineers will play a key role in ensuring the quality and reliability of critical enterprise capabilities across Sanoma Learning.

• Design, develop, and maintain automated tests for APIs, data pipelines, and platform services.

• Ensure data quality and correctness across content ingestion, enrichment, and delivery flows.

• Validate JSON schemas and shared data models across multiple consuming systems.

• Test REST APIs, including contract, integration, and regression testing.

• Verify data persistence and transformations across relational databases and object storage.

• Monitor and validate platform behavior using logs, metrics, and alerts.

• Collaborate with product owners and engineers to contribute to quality practices early in the development lifecycle.

Key Requirements

• Hands-on experience with Python and pytest for automated testing (at least 2 years).

• Experience with API testing (FastAPI-based services).

• Experience with AWS services, in particular: S3, Aurora, Glue, Step Functions, CloudWatch.

• Good SQL skills and experience validating data stored in relational databases.

• Experience with JSON data validation, schema checks, and contract testing.

• English proficiency at B2/C1 level.

Nice to Have

• Understanding of AI or ML-driven search, content enrichment, or recommendation systems.

• Experience with vector databases.

• Experience with Bitbucket Pipelines or similar CI/CD tooling.

• Experience validating LLM or AI-driven outputs, including quality, consistency, and determinism checks.