QA Engineer, Data

At Bridgenext, we engineer Growth Operating Systems. Most enterprises have spent millions on their revenue stack and still aren't seeing the growth they expected.They have tools that function, but no system that wins. We help growth-hungry companies close that gap by turning fragmented platforms, siloed teams, and disconnected data into one integrated Growth OS. More than a technology company or marketing agency, we're a global digital consultancy with experts in engineering, data, AI, creative and more.

Our teams are made up of experts who believe in engineering impact, starting with putting people at the center of everything we do. Every team member directly shapes our work, culture, and values. Nothing matters more to us than a kind, respectful, fulfilling environment that supports everyone. Our flexible, inclusive culture gives you the autonomy, resources, and opportunities to thrive.

We are looking for a talented and experienced QA Engineer to join our team in Canada, Vancouver, Calgary or Toronto.

The client needs a QA Engineer to own quality across its data pipelines, platforms and applications. The data stack includes Snowflake, SQL, dbt, AWS or Azure, and Airflow or Dagster. Some standard tests already exist, including 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 you will work with client’s data engineers, platform engineers, the project manager and other development squads to build a quality-first delivery culture.

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 specific use cases and business requirements, and run them inside  pipeline CI/CD
• Extend the existing dbt and Python data quality tests that are already in place
• Build and maintain automated test suites (unit, integration, regression and end-toend) 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 that automation does not cover yet
• 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) 
• Join root cause analysis for production incidents and close the gaps in test coverage
• Work with engineering, product and analytics teams to set acceptance criteria and quality gates
• Keep clear test plans, test cases, defect reports and regression suites

Workplace:   This is a hybrid contract position, working 2 days per week in office in Vancouver, Calgary, or Toronto. Candidates must live within commuting distance of one of these offices.  Working hours: Standard business hours in the candidate's local time zone. Pacific, Mountain and Eastern are all acceptable.

Must Have Skills:

• 4 to 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 a clear ability to ramp up on dbt in the first two weeks
• Test automation framework experience: 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
• Clear spoken and written English for daily work with data engineers, the project manager and development squads

Preferred Skills:

• Airflow or Dagster experience, since both are in the client’s stack
• AWS or Azure experience
• Datadog dashboards (preferred), Grafana is also acceptable
• Great Expectations or Soda Core (these are not in use at client today)
• Data contract and schema validation experience
• Experience in a financial institution or other regulated environment

Professional Skills:

• Strong English communication skills
• Strong analytical and troubleshooting skills with close attention to detail
• Strong team and individual player
• Maintains composure during all types of situations and is collaborative by nature
• High standards of professionalism, consistently producing high quality results
• Self-sufficient, independent requiring very little supervision or intervention
• Demonstrate flexibility and openness to bring creative solutions to address issues 

Bridgenext is an Equal Opportunity Employer

Canadian citizens and those authorized to work in Canada are encouraged to apply

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