détails d'emploi
We are seeking a highly accomplished Senior DataOps/Cloud Data Engineer for an enterprise-level contract opportunity based in Toronto. In this role, you will take on a premier data engineering and platform architecture capacity, specializing in designing, building, and optimizing cloud data pipelines, Lakehouse architectures, and automated DataOps workflows.
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As a principal cloud data engineer, you will bridge the gap between legacy database systems, modern cloud data platforms, and downstream analytics consumers. Operating fully onsite in Toronto, you will lead the migration of legacy ETL workflows to Azure Data Factory and Databricks ELT, implement Medallion Architecture principles (Delta Lake), tune high-volume SQL/Python data pipelines, and enforce strict data governance and security controls. This position demands a seasoned technical authority who can manage very large data (VLD) environments, automate CI/CD deployments, and deliver scalable Data-as-a-Service solutions.
Location: Toronto, ON
Assignment Type: Onsite (5 days per week)
Contract Duration: 12-month contract (with potential for extension)
Advantages
High-Visibility Modernization: Lead complex ELT migrations transitioning legacy Informatica workflows to Databricks and Azure Data Factory.
Cutting-Edge Medallion Architecture: Design and optimize multi-tiered Lakehouses using Medallion Architecture (Bronze, Silver, Gold) and Delta Lake storage.
Comprehensive DataOps Stack: Deepen technical expertise across Python, SQL, Lakeflow, Databricks, Azure Data Factory, Microsoft Fabric, and Azure DevOps CI/CD.
Enterprise Security & Governance: Implement data lineage tracing, data masking/anonymization, and Microsoft Entra ID role-based access control (RBAC).
Responsibilities
Design, develop, tune, and maintain Azure Data Factory and Databricks data pipelines connecting relational source databases to modern Lakehouses.
Architect and optimize multi-tiered data models using Medallion Architecture principles and Delta Lake indexing, parallelism, and partitioning strategies.
Migrate legacy Informatica ETL code, embedded SQL, and PL/SQL procedures into modern Azure Data Factory and Databricks ELT workflows.
Design and establish secure data connections bridging Databricks Medallion layers with on-premises databases for downstream operational and analytics consumption.
Build automated CI/CD pipelines and DataOps orchestration workflows using Python, Unix scripting, and Azure DevOps.
Implement robust data quality frameworks, including automated data validation, profiling, cleansing, and pipeline health monitoring.
Enforce enterprise data governance standards, authoring conceptual, logical, and physical dimensional data models (star and snowflake schemas).
Integrate Microsoft Entra ID for secure authentication, authorization, and role-based access controls across cloud data repositories.
Develop and maintain automated data lineage reports to provide end-to-end traceability of data movement and transformations.
Apply data anonymization and masking techniques to protect sensitive and regulated data in compliance with privacy policies.
Perform defect investigations, root-cause troubleshooting, performance tuning, and production support for high-volume data streams.
Qualifications
Core Technical & DataOps Requirements
Cloud Data Pipeline Expertise: Extensive experience developing ETL/ELT processes using Informatica, Azure Data Factory, and Databricks (Python, Lakeflow, SQL optimization).
Databricks & Medallion Architecture: Proven hands-on experience designing and deploying solutions with Databricks using Medallion Architecture principles and Delta Lake storage.
Programming Languages & Tooling: Mastery of Python, SQL, T-SQL, PL/SQL, Informatica, ADF, SSIS, and Microsoft Fabric to construct automation scripts, jobs, and data pipelines.
Pipeline Orchestration & Parallelism: Strong background in workflow orchestration, pipeline deployment, Delta Lake indexing, parallel processing, and Data-as-a-Service (DaaS) management.
Data Modeling & Migration: Demonstrated experience designing star-schema dimensional models and executing migration of Very Large Data (VLD) from OLTP/OLAP environments to cloud SaaS/PaaS/IaaS platforms.
CI/CD & DevOps Automation: Direct experience with Continuous Integration/Continuous Deployment (CI/CD), Azure DevOps, and DataOps performance monitoring.
Preferred Assets & Certifications
Professional Certifications: Databricks Certified Data Engineer or Microsoft Certified: Fabric Analytics Engineer Associate designations are considered strong assets.
Public Sector Context: Experience working in large-scale public sector organizations or comparable enterprise environments.
Accessibility Standards: Familiarity with AODA/WCAG compliance standards for digital deliverables.
Soft Skills & Delivery Experience
Analytical Problem Solving: Superior diagnostic capabilities to troubleshoot complex data pipeline bottlenecks, perform root-cause analysis, and optimize queries.
Consultative Communication: Excellent written, verbal, and presentation skills to explain technical choices and collaborate effectively across business and IT squads.
Agile Collaboration: Proven ability to work collaboratively in fast-paced Agile environments, managing competing priorities and meeting strict delivery deadlines.
Summary
If you're interested in the "Senior DataOps/Cloud Data Engineer" role based in Toronto, 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.
This posting is for existing and upcoming vacancies.
voir plus
We are seeking a highly accomplished Senior DataOps/Cloud Data Engineer for an enterprise-level contract opportunity based in Toronto. In this role, you will take on a premier data engineering and platform architecture capacity, specializing in designing, building, and optimizing cloud data pipelines, Lakehouse architectures, and automated DataOps workflows.
As a principal cloud data engineer, you will bridge the gap between legacy database systems, modern cloud data platforms, and downstream analytics consumers. Operating fully onsite in Toronto, you will lead the migration of legacy ETL workflows to Azure Data Factory and Databricks ELT, implement Medallion Architecture principles (Delta Lake), tune high-volume SQL/Python data pipelines, and enforce strict data governance and security controls. This position demands a seasoned technical authority who can manage very large data (VLD) environments, automate CI/CD deployments, and deliver scalable Data-as-a-Service solutions.
Location: Toronto, ON
Assignment Type: Onsite (5 days per week)
Contract Duration: 12-month contract (with potential for extension)
Advantages
...
High-Visibility Modernization: Lead complex ELT migrations transitioning legacy Informatica workflows to Databricks and Azure Data Factory.
Cutting-Edge Medallion Architecture: Design and optimize multi-tiered Lakehouses using Medallion Architecture (Bronze, Silver, Gold) and Delta Lake storage.
Comprehensive DataOps Stack: Deepen technical expertise across Python, SQL, Lakeflow, Databricks, Azure Data Factory, Microsoft Fabric, and Azure DevOps CI/CD.
Enterprise Security & Governance: Implement data lineage tracing, data masking/anonymization, and Microsoft Entra ID role-based access control (RBAC).
Responsibilities
Design, develop, tune, and maintain Azure Data Factory and Databricks data pipelines connecting relational source databases to modern Lakehouses.
Architect and optimize multi-tiered data models using Medallion Architecture principles and Delta Lake indexing, parallelism, and partitioning strategies.
Migrate legacy Informatica ETL code, embedded SQL, and PL/SQL procedures into modern Azure Data Factory and Databricks ELT workflows.
Design and establish secure data connections bridging Databricks Medallion layers with on-premises databases for downstream operational and analytics consumption.
Build automated CI/CD pipelines and DataOps orchestration workflows using Python, Unix scripting, and Azure DevOps.
Implement robust data quality frameworks, including automated data validation, profiling, cleansing, and pipeline health monitoring.
Enforce enterprise data governance standards, authoring conceptual, logical, and physical dimensional data models (star and snowflake schemas).
Integrate Microsoft Entra ID for secure authentication, authorization, and role-based access controls across cloud data repositories.
Develop and maintain automated data lineage reports to provide end-to-end traceability of data movement and transformations.
Apply data anonymization and masking techniques to protect sensitive and regulated data in compliance with privacy policies.
Perform defect investigations, root-cause troubleshooting, performance tuning, and production support for high-volume data streams.
Qualifications
Core Technical & DataOps Requirements
Cloud Data Pipeline Expertise: Extensive experience developing ETL/ELT processes using Informatica, Azure Data Factory, and Databricks (Python, Lakeflow, SQL optimization).
Databricks & Medallion Architecture: Proven hands-on experience designing and deploying solutions with Databricks using Medallion Architecture principles and Delta Lake storage.
Programming Languages & Tooling: Mastery of Python, SQL, T-SQL, PL/SQL, Informatica, ADF, SSIS, and Microsoft Fabric to construct automation scripts, jobs, and data pipelines.
Pipeline Orchestration & Parallelism: Strong background in workflow orchestration, pipeline deployment, Delta Lake indexing, parallel processing, and Data-as-a-Service (DaaS) management.
Data Modeling & Migration: Demonstrated experience designing star-schema dimensional models and executing migration of Very Large Data (VLD) from OLTP/OLAP environments to cloud SaaS/PaaS/IaaS platforms.
CI/CD & DevOps Automation: Direct experience with Continuous Integration/Continuous Deployment (CI/CD), Azure DevOps, and DataOps performance monitoring.
Preferred Assets & Certifications
Professional Certifications: Databricks Certified Data Engineer or Microsoft Certified: Fabric Analytics Engineer Associate designations are considered strong assets.
Public Sector Context: Experience working in large-scale public sector organizations or comparable enterprise environments.
Accessibility Standards: Familiarity with AODA/WCAG compliance standards for digital deliverables.
Soft Skills & Delivery Experience
Analytical Problem Solving: Superior diagnostic capabilities to troubleshoot complex data pipeline bottlenecks, perform root-cause analysis, and optimize queries.
Consultative Communication: Excellent written, verbal, and presentation skills to explain technical choices and collaborate effectively across business and IT squads.
Agile Collaboration: Proven ability to work collaboratively in fast-paced Agile environments, managing competing priorities and meeting strict delivery deadlines.
Summary
If you're interested in the "Senior DataOps/Cloud Data Engineer" role based in Toronto, 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.
This posting is for existing and upcoming vacancies.
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