QA Engineer

RITS Group is an IT company delivering top-quality software development services for both the Polish and international markets. We are proud to be among the fastest-growing technology companies, driven by innovation, excellence, and continuous improvement. Our organizational culture is built on trust, collaboration, and a strong focus on quality.

We are currently looking for a QA Engineer (Data & Analytics) who will play a key role in ensuring the accuracy, reliability, and performance of our data products and analytics platforms.

In this role, you will lead data quality assurance activities across the entire software development lifecycle, with a strong focus on data integrity, pipeline validation, analytics testing, and end-to-end system verification.

You will work closely with data engineers, analysts, and other stakeholders to embed quality best practices into data pipelines, transformations, and reporting processes—ensuring that business-critical data and insights remain accurate, consistent, and trustworthy.

Responsibilities:

Data Quality & Testing

● Define and implement data quality validation strategies across pipelines, data lakes, and warehouses.

● Verify data consistency, integrity, completeness, and transformations from source systems to downstream analytics platforms like ThoughtSpot and Databricks.

● Develop and execute end-to-end data validation and reconciliation tests to ensure accurate reporting and insights.

● Work with engineers to implement data contracts, schema validations, and anomaly detection mechanisms using tools like Great Expectations.

● Perform exploratory data testing, investigating discrepancies and ensuring data correctness across multiple environments.

● Validate data migrations, ensuring business continuity and minimal disruption during cloud transitions (e.g. Databricks to GCP migration).

Quality Leadership & Collaboration

● Collaborate with data engineers, analysts, and software engineers to establish and enforce data quality standards.

● Work closely with teams to define data validation criteria, SLAs for data freshness, and monitoring strategies.

● Participate in design and planning discussions to ensure data observability, lineage tracking, and governance are embedded early.

● Champion best practices in data testing, monitoring, and alerting, ensuring quick detection and resolution of data quality issues.

Continuous Improvement & Technical Excellence

● Leverage data quality metrics to identify trends, gaps, and areas for improvement.

● Explore and advocate for new tools, frameworks, and methodologies to enhance data testing efficiency.