4-6 years in QA or test engineering with a focus on data pipeline or ETL testing.
Strong SQL skills for data validation and query-based testing.
Experience with Snowflake or a comparable cloud data warehouse, with a quick ramp-up ability.
Proficiency in Python for test automation and data validation scripts.
Familiarity with dbt tests or strong SQL and Python skills, with a quick learning curve for dbt.
Experience with test automation frameworks like Pytest, Selenium, or similar.
CI/CD integration experience with tools like Jenkins or GitLab CI.
Responsibilities
Design test strategies for data pipelines to ensure accuracy and completeness.
Build new automated data quality checks in dbt and Python based on business requirements.
Extend existing dbt and Python data quality tests.
Maintain automated test suites for applications, including unit and integration tests.
Validate orchestration workflows in Airflow or Dagster for correctness and failure handling.
Integrate automated testing into CI/CD pipelines for reliable feedback.
Conduct exploratory and manual testing for uncovered edge cases.
Benefits
Annual performance evaluation and increment.
Weekly two holidays.
General leave of 10 days.
Public holidays as per client holiday calendar.
Full Job Description
About the job Data Driven QA Automation Engineer
Job Context:
Riseup Labs is seeking a QA Engineer (who lives in Canada near at Vancouver, Calgary or Toronto) to own quality across their data pipelines, platforms and applications. The data stack includes Snowflake, SQL, dbt, AWS or Azure, and Airflow or Dagster. Some standard tests already exist (dbt tests and Python data quality checks). Most of the work is building new tests for specific use cases and business requirements. You will design testing strategies that catch issues before production and work closely with data engineers, platform engineers, the project manager and other development squads to build a quality-first delivery culture.
Job Responsibilities:
Design test strategies for data pipelines, validating accuracy, completeness, schema consistency and transformation logic across ETL and ELT workflows in Snowflake.
Build new automated data quality checks in dbt tests and Python, based on use cases and business requirements, and run them inside pipeline CI/CD.
Extend the dbt and Python data quality tests already in place.
Build and maintain automated test suites (unit, integration, regression and end-to-end) for applications.
Validate orchestration workflows in Airflow or Dagster for correctness, idempotency and failure handling.
Add automated testing to CI/CD pipelines for fast, reliable feedback.
Run exploratory and manual testing for edge cases not yet covered by automation.
Work with data engineers to define and enforce data contracts and schema validation between upstream and downstream systems.
Build and monitor quality dashboards (test coverage, defect trends, pipeline reliability) in Datadog. Grafana is acceptable, but Datadog is preferred.
Take part in root cause analysis for production incidents and close gaps in test coverage.
Agree acceptance criteria and quality gates with engineering, product and analytics teams.
Maintain clear test plans, test cases, defect reports and regression suites.
Requirements:
4-6 years in QA or test engineering, with hands-on data pipeline or ETL testing.
Strong SQL for data validation and query-based testing.
Snowflake, or a comparable cloud data warehouse with a clear ability to ramp up on Snowflake quickly.
Python for test automation and data validation scripts.
dbt tests, or strong SQL and Python with the ability to ramp up on dbt within the first two weeks.
Experience with a test automation framework: Pytest, Selenium, Cypress, Playwright or similar.
CI/CD integration of automated tests: Jenkins, GitLab CI, CircleCI or similar.
Working awareness of data privacy, security and PII handling.
Strong analytical and troubleshooting skills with close attention to detail.
Clear spoken and written English.
Lives in Canada with valid work authorization, within 1 hour of the Vancouver, Calgary or Toronto office, and able to work in the office 2 days a week. Travel expenses are not covered.
Additional Requirements:
Airflow or Dagster experience
AWS or Azure experience
Datadog dashboards (Grafana also acceptable)
Great Expectations or Soda Core
Data contract and schema validation experience
Experience in a financial institution or other regulated environment
Quality-first mindset: raises risks early without waiting to be asked.
Works well with data engineers, platform engineers, product managers and other squads.
Builds test cases from business requirements without needing every detail spelled out.
Writes clear documentation others can reuse.
Makes sound decisions independently and explains the reasoning.
Handles sensitive data carefully.
Workplace:
Hybrid (2 days a week on office)
Working Hours:
09.00 AM to 06.00 PM (Pacific, Mountain or Eastern)
Salary:
Negotiable (Based on experience and skills)
Compensation & Other Benefits (Applicable for International Candidate):
Annual Performance Evaluation and Increment
Weekly 2 Holidays
General Leave: 10 days
Public Holidays as per Client Holiday Calendar
The Application Process:
Telephone Round.
Interview with the Tech Team & Talent Acquisition Team.
Job offer.
NB: Only shortlisted candidates will be communicated in the recruitment process.
