We are seeking a highly technical, delivery-focused Data Engineer with a specialized passion for data quality automation and infrastructure optimization. In this role, you will be responsible for the integration, multi-dimensional modeling, and orchestration of complex, large-scale data environments hosted natively on Snowflake and Databricks.
...
This position sits at the intersection of core data platform engineering and data governance. Beyond building resilient data pipelines, you will design and implement custom, reusable automation testing frameworks to certify data quality across critical data domains. If you are an autonomous self-starter who loves digging deep into execution logs, profiling data lineage, and optimizing distributed data system queries, this role offers an exceptional playground for your skills.
Location: Vancouver, BC (Hybrid – 4 days per week onsite)
Contract Duration: 6-month contract with a high likelihood of extension
Work Schedule: Standard business hours, collaborating across enterprise engineering streams.
Advantages
Cutting-Edge Stack Control: Take direct technical ownership over an advanced modern cloud data stack combining Databricks clusters and Snowflake endpoints.
High-Impact Architecture: Build a net-new automated data quality framework that will protect data integrity across a growing global organization.
Strong Potential for Longevity: Step into an initial 6-month contract with highly anticipated extensions as the data engineering ecosystem continues to expand.
Premium Collaborative Culture: Work out of a highly creative, values-led, and people-first onsite workspace in beautiful Vancouver.
Responsibilities
1. Data Integration, Modeling & Pipeline Engineering
Identify, design, and implement end-to-end integration, structural data modeling, and data warehousing solutions.
Build and scale robust ETL/ELT pipelines to process large-scale, complex datasets hosted across Snowflake and Databricks platforms.
Monitor, isolate, and troubleshoot active data engineering incidents and transactional pipeline anomalies in runtime environments.
2. Custom Data Quality & Automation Frameworks
Design and implement comprehensive data quality solutions for streaming and batch pipelines, ensuring complete adherence to enterprise data standards.
Develop custom, reusable automated testing frameworks to validate data freshness, schema consistency, and accuracy across critical business domains.
Act as an internal evangelist: educate, enable, and onboard cross-functional technical teams onto centralized data quality toolsets while enforcing industry best practices.
3. Performance Tuning & Advanced Diagnostics
Debug complex, distributed data processing systems using runtime logs, execution profiles, and end-to-end data lineage tracking to isolate root causes of technical debt.
Optimize the compute performance of complex SQL queries, Spark jobs, and cloud data pipelines.
Implement storage optimization strategies within structural architectural constraints to lower platform compute costs.
4. Cross-Functional Architecture Collaboration
Partner with Data Architects, Solution Deliverers, and Product Managers to define data quality requirements, evaluate technical feasibility, formulate effort estimations, and outline project scopes.
Author and maintain high-quality technical documentation, process maps, and runbooks to support long-term operational maintainability.
Qualifications
Core Experience: 4–7 years of proven, hands-on experience as a Data Engineer, Big Data Developer, or in a closely related technical delivery role.
Platform Mastery: Direct, operational experience utilizing Databricks, PySpark, and Snowflake data platforms.
Programming & Query Stacks: High proficiency in SQL paired with strong programmatic capabilities in Python (preferred), Java, or Scala.
Cloud & DevOps Infrastructure: Extensive experience leveraging Azure Cloud Services (Azure Data Factory, Azure Functions, AKS, Docker).
Orchestration & CI/CD Tooling: Practical experience with data orchestrators (Apache Airflow or Azure Data Factory) and continuous integration pipelines (Jenkins, Azure DevOps, or GitLab).
Data Fundamentals: Deep conceptual understanding of data management fundamentals, storage paradigms, star/snowflake schemas, and distributed data architectures.
Education: Bachelor’s degree in Computer Science, Software Engineering, or an equivalent technical field.
Preferred Skills & Personal Attributes
Domain Context: Direct technical experience working within the Retail or E-commerce domain is highly preferred.
Diagnostic Mindset: An inherently curious, self-directed analytical approach with a drive to uncover exactly why systems behave a certain way.
Communication Execution: High transparency and clarity in writing technical specifications and communicating complex data risks to multi-disciplinary teams.
Summary
If you are a seasoned Data Engineer who excels at optimizing distributed pipelines and building automated solutions to eliminate data quality issues, 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 technical, delivery-focused Data Engineer with a specialized passion for data quality automation and infrastructure optimization. In this role, you will be responsible for the integration, multi-dimensional modeling, and orchestration of complex, large-scale data environments hosted natively on Snowflake and Databricks.
This position sits at the intersection of core data platform engineering and data governance. Beyond building resilient data pipelines, you will design and implement custom, reusable automation testing frameworks to certify data quality across critical data domains. If you are an autonomous self-starter who loves digging deep into execution logs, profiling data lineage, and optimizing distributed data system queries, this role offers an exceptional playground for your skills.
Location: Vancouver, BC (Hybrid – 4 days per week onsite)
Contract Duration: 6-month contract with a high likelihood of extension
Work Schedule: Standard business hours, collaborating across enterprise engineering streams.
Advantages
Cutting-Edge Stack Control: Take direct technical ownership over an advanced modern cloud data stack combining Databricks clusters and Snowflake endpoints.
...
High-Impact Architecture: Build a net-new automated data quality framework that will protect data integrity across a growing global organization.
Strong Potential for Longevity: Step into an initial 6-month contract with highly anticipated extensions as the data engineering ecosystem continues to expand.
Premium Collaborative Culture: Work out of a highly creative, values-led, and people-first onsite workspace in beautiful Vancouver.
Responsibilities
1. Data Integration, Modeling & Pipeline Engineering
Identify, design, and implement end-to-end integration, structural data modeling, and data warehousing solutions.
Build and scale robust ETL/ELT pipelines to process large-scale, complex datasets hosted across Snowflake and Databricks platforms.
Monitor, isolate, and troubleshoot active data engineering incidents and transactional pipeline anomalies in runtime environments.
2. Custom Data Quality & Automation Frameworks
Design and implement comprehensive data quality solutions for streaming and batch pipelines, ensuring complete adherence to enterprise data standards.
Develop custom, reusable automated testing frameworks to validate data freshness, schema consistency, and accuracy across critical business domains.
Act as an internal evangelist: educate, enable, and onboard cross-functional technical teams onto centralized data quality toolsets while enforcing industry best practices.
3. Performance Tuning & Advanced Diagnostics
Debug complex, distributed data processing systems using runtime logs, execution profiles, and end-to-end data lineage tracking to isolate root causes of technical debt.
Optimize the compute performance of complex SQL queries, Spark jobs, and cloud data pipelines.
Implement storage optimization strategies within structural architectural constraints to lower platform compute costs.
4. Cross-Functional Architecture Collaboration
Partner with Data Architects, Solution Deliverers, and Product Managers to define data quality requirements, evaluate technical feasibility, formulate effort estimations, and outline project scopes.
Author and maintain high-quality technical documentation, process maps, and runbooks to support long-term operational maintainability.
Qualifications
Core Experience: 4–7 years of proven, hands-on experience as a Data Engineer, Big Data Developer, or in a closely related technical delivery role.
Platform Mastery: Direct, operational experience utilizing Databricks, PySpark, and Snowflake data platforms.
Programming & Query Stacks: High proficiency in SQL paired with strong programmatic capabilities in Python (preferred), Java, or Scala.
Cloud & DevOps Infrastructure: Extensive experience leveraging Azure Cloud Services (Azure Data Factory, Azure Functions, AKS, Docker).
Orchestration & CI/CD Tooling: Practical experience with data orchestrators (Apache Airflow or Azure Data Factory) and continuous integration pipelines (Jenkins, Azure DevOps, or GitLab).
Data Fundamentals: Deep conceptual understanding of data management fundamentals, storage paradigms, star/snowflake schemas, and distributed data architectures.
Education: Bachelor’s degree in Computer Science, Software Engineering, or an equivalent technical field.
Preferred Skills & Personal Attributes
Domain Context: Direct technical experience working within the Retail or E-commerce domain is highly preferred.
Diagnostic Mindset: An inherently curious, self-directed analytical approach with a drive to uncover exactly why systems behave a certain way.
Communication Execution: High transparency and clarity in writing technical specifications and communicating complex data risks to multi-disciplinary teams.
Summary
If you are a seasoned Data Engineer who excels at optimizing distributed pipelines and building automated solutions to eliminate data quality issues, 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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