We are seeking a highly analytical and technical SDET Quality Engineer specializing in Data Quality and Observability to join our team in Vancouver. In this role, you will act as the primary owner of data validation frameworks, ensuring data integrity, completeness, and schema consistency across large-scale distributed data systems.
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This is not a traditional front-end or API testing role; it requires a specialist who understands data infrastructure, pipelines, and cloud data warehouses. You will build automated testing strategies, debug complex transactional anomalies, profile query performance, and drive the institutional adoption of modern data observability tools across our global engineering streams.
Location: Vancouver, BC (Hybrid – 4 days per week onsite)
Contract Duration: 6-month contract with high likelihood of extension
Work Schedule: Standard business hours, working closely with cross-functional data teams.
Advantages
Cutting-Edge Tech Stack: Take ownership of data quality across a world-class cloud data ecosystem leveraging Databricks, PySpark, and advanced observability tools.
Broad Organizational Impact: Act as an internal consultant and subject matter expert, empowering multiple engineering lines to upgrade their data governance.
Strong Extension Potential: Join a multi-phase program with a 6-month contract that is highly likely to extend long-term.
Vibrant West-Coast Setting: Work in a highly collaborative, values-driven culture based out of a premier Vancouver office hub.
Responsibilities
1. Data Validation & Automation Frameworks
Implement comprehensive, automated data validation and testing strategies, including schema validation, structural data quality checks, and end-to-end reconciliation tests.
Ensure consistent, automated data quality monitoring and alert coverage across critical enterprise data domains.
Define, continuously evolve, and document enterprise data quality standards, compliance rules, and testing best practices.
2. Monitoring, Observability & Enablement
Drive org-wide adoption of data quality monitoring and observability tools by actively onboarding, training, and enabling cross-functional engineering teams.
Monitor, isolate, and troubleshoot active data quality anomalies in runtime environments.
Support data transparency by maintaining robust technical documentation, process maps, data lineage records, and data dictionaries.
3. Advanced Debugging & Performance Optimization
Deeply debug complex data inconsistencies across distributed systems using execution logs, profiling utilities, and data lineage tracking to isolate root causes.
Test, profile, and optimize the performance of complex SQL queries, large-scale data pipelines, and cloud storage systems within structural architectural constraints.
4. Collaboration & Delivery Governance
Partner with Data Architects, Analytics Engineers, and Business Analysts to define data quality requirements, review technical feasibility, formulate effort estimations, and establish testing scopes.
Act as an autonomous, high-accountability owner of the data testing domain while pragmatically navigating architectural ambiguity.
Qualifications
Core Experience: 4–7 years of dedicated experience as a Data Quality Engineer, Data SDET, or Data Engineer with a heavy focus on automated verification.
Data Processing Platforms: Hands-on operational experience with Databricks, PySpark, and the Snowflake data platform.
Programming & Query Stacks: High proficiency in SQL paired with strong programmatic capabilities in Python (preferred), Java, or Scala.
Cloud Architecture: Practical experience interacting with enterprise cloud data systems (Azure, AWS, or GCP).
Data Fundamentals: Solid conceptual understanding of data management principles, storage models, ETL/ELT pipelines, dimensional data modeling, and modern data warehouse architecture.
CI/CD & Devops: Active experience integrating automated data test suites into continuous delivery pipelines using Jenkins, Azure DevOps, or GitLab.
Education: Bachelor’s degree in Computer Science, Software Engineering, or an equivalent technical field.
Preferred Skills & Personal Attributes
Domain Context: Direct experience working within the Retail or E-commerce industry is highly preferred.
Mindset: An inherently curious, self-starting diagnostic approach with a passion for uncovering precisely why systems behave a certain way.
Communication: Exceptional clarity and transparency in technical writing and verbal communications.
Summary
If you are a Data SDET who loves to profile complex data flows, write production-ready test automation, and enforce data integrity at scale, we encourage you to apply online at www.randstad.ca. Only qualified candidates will be contacted for the next steps. We look forward to hearing from you!
Randstad Canada is committed to fostering a workforce reflective of all peoples of Canada. As a result, we are committed to developing and implementing strategies to increase the equity, diversity and inclusion within the workplace by examining our internal policies, practices, and systems throughout the entire lifecycle of our workforce, including its recruitment, retention and advancement for all employees. In addition to our deep commitment to respecting human rights, we are dedicated to positive actions to affect change to ensure everyone has full participation in the workforce free from any barriers, systemic or otherwise, especially equity-seeking groups who are usually underrepresented in Canada's workforce, including those who identify as women or non-binary/gender non-conforming; Indigenous or Aboriginal Peoples; persons with disabilities (visible or invisible) and; members of visible minorities, racialized groups and the LGBTQ2+ community.
Randstad Canada is committed to creating and maintaining an inclusive and accessible workplace for all its candidates and employees by supporting their accessibility and accommodation needs throughout the employment lifecycle. We ask that all job applications please identify any accommodation requirements by sending an email to accessibility@randstad.ca to ensure their ability to fully participate in the interview process.
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We are seeking a highly analytical and technical SDET Quality Engineer specializing in Data Quality and Observability to join our team in Vancouver. In this role, you will act as the primary owner of data validation frameworks, ensuring data integrity, completeness, and schema consistency across large-scale distributed data systems.
This is not a traditional front-end or API testing role; it requires a specialist who understands data infrastructure, pipelines, and cloud data warehouses. You will build automated testing strategies, debug complex transactional anomalies, profile query performance, and drive the institutional adoption of modern data observability tools across our global engineering streams.
Location: Vancouver, BC (Hybrid – 4 days per week onsite)
Contract Duration: 6-month contract with high likelihood of extension
Work Schedule: Standard business hours, working closely with cross-functional data teams.
Advantages
Cutting-Edge Tech Stack: Take ownership of data quality across a world-class cloud data ecosystem leveraging Databricks, PySpark, and advanced observability tools.
...
Broad Organizational Impact: Act as an internal consultant and subject matter expert, empowering multiple engineering lines to upgrade their data governance.
Strong Extension Potential: Join a multi-phase program with a 6-month contract that is highly likely to extend long-term.
Vibrant West-Coast Setting: Work in a highly collaborative, values-driven culture based out of a premier Vancouver office hub.
Responsibilities
1. Data Validation & Automation Frameworks
Implement comprehensive, automated data validation and testing strategies, including schema validation, structural data quality checks, and end-to-end reconciliation tests.
Ensure consistent, automated data quality monitoring and alert coverage across critical enterprise data domains.
Define, continuously evolve, and document enterprise data quality standards, compliance rules, and testing best practices.
2. Monitoring, Observability & Enablement
Drive org-wide adoption of data quality monitoring and observability tools by actively onboarding, training, and enabling cross-functional engineering teams.
Monitor, isolate, and troubleshoot active data quality anomalies in runtime environments.
Support data transparency by maintaining robust technical documentation, process maps, data lineage records, and data dictionaries.
3. Advanced Debugging & Performance Optimization
Deeply debug complex data inconsistencies across distributed systems using execution logs, profiling utilities, and data lineage tracking to isolate root causes.
Test, profile, and optimize the performance of complex SQL queries, large-scale data pipelines, and cloud storage systems within structural architectural constraints.
4. Collaboration & Delivery Governance
Partner with Data Architects, Analytics Engineers, and Business Analysts to define data quality requirements, review technical feasibility, formulate effort estimations, and establish testing scopes.
Act as an autonomous, high-accountability owner of the data testing domain while pragmatically navigating architectural ambiguity.
Qualifications
Core Experience: 4–7 years of dedicated experience as a Data Quality Engineer, Data SDET, or Data Engineer with a heavy focus on automated verification.
Data Processing Platforms: Hands-on operational experience with Databricks, PySpark, and the Snowflake data platform.
Programming & Query Stacks: High proficiency in SQL paired with strong programmatic capabilities in Python (preferred), Java, or Scala.
Cloud Architecture: Practical experience interacting with enterprise cloud data systems (Azure, AWS, or GCP).
Data Fundamentals: Solid conceptual understanding of data management principles, storage models, ETL/ELT pipelines, dimensional data modeling, and modern data warehouse architecture.
CI/CD & Devops: Active experience integrating automated data test suites into continuous delivery pipelines using Jenkins, Azure DevOps, or GitLab.
Education: Bachelor’s degree in Computer Science, Software Engineering, or an equivalent technical field.
Preferred Skills & Personal Attributes
Domain Context: Direct experience working within the Retail or E-commerce industry is highly preferred.
Mindset: An inherently curious, self-starting diagnostic approach with a passion for uncovering precisely why systems behave a certain way.
Communication: Exceptional clarity and transparency in technical writing and verbal communications.
Summary
If you are a Data SDET who loves to profile complex data flows, write production-ready test automation, and enforce data integrity at scale, we encourage you to apply online at www.randstad.ca. Only qualified candidates will be contacted for the next steps. We look forward to hearing from you!
Randstad Canada is committed to fostering a workforce reflective of all peoples of Canada. As a result, we are committed to developing and implementing strategies to increase the equity, diversity and inclusion within the workplace by examining our internal policies, practices, and systems throughout the entire lifecycle of our workforce, including its recruitment, retention and advancement for all employees. In addition to our deep commitment to respecting human rights, we are dedicated to positive actions to affect change to ensure everyone has full participation in the workforce free from any barriers, systemic or otherwise, especially equity-seeking groups who are usually underrepresented in Canada's workforce, including those who identify as women or non-binary/gender non-conforming; Indigenous or Aboriginal Peoples; persons with disabilities (visible or invisible) and; members of visible minorities, racialized groups and the LGBTQ2+ community.
Randstad Canada is committed to creating and maintaining an inclusive and accessible workplace for all its candidates and employees by supporting their accessibility and accommodation needs throughout the employment lifecycle. We ask that all job applications please identify any accommodation requirements by sending an email to accessibility@randstad.ca to ensure their ability to fully participate in the interview process.
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