QA Data Engineer (Snowflake)

Location: Portugal – Remote. The posting header also references Brussels, Belgium (Hybrid).

No salary specified.

Company

hirify.global is represented by a Delivery Consultant for this contract role.

What you will do

• Build and implement a reusable data testing and quality framework for a Snowflake data programme.
• Assess data testing tools and recommend a suitable solution.
• Review and extend dbt test coverage across Acquisition and Curation models.
• Translate data quality requirements into development-time and production tests covering completeness, accuracy, timeliness, consistency, uniqueness, and validity.
• Integrate tests into CI/CD and support developer self-service and scheduled production assurance testing.
• Monitor coverage, defects, alerts, and Snowflake compute usage; document the framework and transfer knowledge to Data Management.

Requirements

• Advanced SQL and strong hands-on dbt experience in a production Snowflake environment.
• Experience with data quality testing frameworks such as dbt-expectations, Great Expectations, Soda, or equivalent.
• Strong Snowflake, data warehousing, and Lakehouse experience, including streams, tasks, time travel, cloning, and semi-structured data.
• CI/CD experience with Jenkins, GitHub Actions, or an equivalent tool, plus Python for test orchestration and scripting.
• Strong understanding of data quality dimensions and testable assertions, including deterministic testing alongside AI/ML model and agent evaluation.
• Strong documentation and stakeholder management skills, with the ability to work across Data Engineering, QA, and Data Management.

Nice to have

• Entity resolution or fuzzy matching experience.
• Data lineage and source-system conflict or precedence experience.
• Experience in risk, financial crime, or financial services.
• AWS, Azure, or GCP experience.
• Snowflake certification.

Culture & Benefits

• Initial six-month contract with possible extensions.
• Close collaboration with Data Engineers, Analysts, QA, and Data Management stakeholders.
• Focus on building maintainable solutions that can be successfully handed over to the Data Management team.