Senior Data Engineer

Hiring Senior Data Engineer – Remote in Canada

Stafingo is hiring on behalf of a client for multiple Senior Data Engineer opportunities. These are long-term contract positions supporting large-scale digital transformation, data modernization, analytics, and enterprise technology initiatives.

We are seeking experienced Senior Data Engineers who can work across modern cloud data platforms, enterprise data integration, analytics, and data governance. The successful candidates will work with cross-functional teams to design scalable data solutions, modernize data pipelines, improve data quality, and enable analytics and reporting capabilities.

Position Details :

• Job Title: Senior Data Engineer
• Number of Resources: 5
• Contracts Available: Up to 10
• Employment Type: Contract
• Location: Remote within Canada
• Potential On-Site Requirement: Occasional meetings or working sessions in Edmonton, Alberta, with reasonable notice
• Start Date: September 14th, 2026
• End Date: September 30th, 2027
• Extension: Up to 24 months
• Pay Range: $90/hour T4 – $110/hour INC
• Submission Deadline: Sunday, August 16th, 2026 – EOD

About the Opportunity :

This is an excellent opportunity for a Senior Data Engineer to contribute to enterprise-scale data modernization initiatives involving cloud platforms, data integration, analytics, reporting, and data governance.

• The successful candidate will work within multidisciplinary teams and collaborate with data architects, developers, analysts, product teams, business stakeholders, and technology professionals to translate complex business requirements into scalable and maintainable data solutions.
• The role offers exposure to a broad range of modern data technologies and environments, including cloud data platforms, data lakes, data warehouses, ETL/ELT frameworks, business intelligence, automation, and emerging AI-enabled data capabilities.

Key Responsibilities :

• Data Engineering & Cloud Data Platforms :
• Design, develop, and maintain scalable data pipelines supporting enterprise data and analytics requirements.
• Build modern ETL/ELT solutions across cloud and on-premises environments.
• Develop data ingestion, transformation, validation, and publishing processes for structured and unstructured data.
• Work with cloud data platforms and technologies such as Microsoft Azure, Azure Data Factory, Databricks, Microsoft Fabric, AWS, and GCP.
• Develop robust data lake, data warehouse, and lakehouse solutions.
• Integrate data from relational databases, NoSQL platforms, APIs, files, and other enterprise data sources.
• Implement reusable data engineering patterns to improve scalability, reliability, maintainability, and performance.

Data Integration & Pipeline Development :

• Design and implement end-to-end data integration workflows.
• Develop transformation logic using SQL, Python, PySpark, notebooks, and modern data processing technologies.
• Work with ETL technologies such as SSIS, Azure Data Factory, Fabric Data Factory, Dataflows, and notebook-based processing.
• Implement pipeline error handling, data validation, logging, monitoring, scheduling, and operational controls.
• Troubleshoot data pipeline failures and resolve data quality and performance issues.
• Optimize high-volume data processing and identify opportunities to eliminate bottlenecks.
• Develop automated processes for schema validation, data mapping, metadata enrichment, and data quality monitoring.
• Apply emerging AI-assisted techniques where appropriate to improve data integration and engineering efficiency.

Data Modeling & Architecture :

• Develop and optimize enterprise data models for analytics and reporting.
• Create dimensional models, including star and snowflake schemas.
• Design fact and dimension tables and curated data marts.
• Optimize data structures and queries for performance, scalability, and usability.
• Collaborate with architects and technical teams to establish appropriate data architecture patterns.
• Support data warehouse, lakehouse, and data lake implementations.

DevOps, Automation & CI/CD :

• Implement CI/CD practices for data engineering solutions.
• Automate deployment, testing, release, and operational processes.
• Work with source control and DevOps tools to support reliable software delivery.
• Implement automated data validation and testing.
• Monitor production pipelines and proactively identify operational issues.
• Contribute to continuous improvement of development, deployment, and support processes.

• Data Governance, Security & Quality
• Support enterprise data governance and data management initiatives.
• Implement data quality checks and validation frameworks.
• Work with metadata, lineage, access controls, and data security requirements.
• Support appropriate management of enterprise data lakes and warehouses.
• Collaborate with data governance and architecture teams to improve data accessibility, consistency, and reliability.
• Ensure data solutions follow applicable organizational security, privacy, and information-management standards.

• Analytics & Business Intelligence
• Analyze complex datasets to identify trends, patterns, anomalies, and opportunities.
• Develop analytical solutions using SQL, Python, DAX, and other analytical technologies.
• Create and enhance Power BI dashboards, reports, KPIs, calculated measures, and analytical models.
• Develop curated datasets and semantic models that enable self-service analytics.
• Support descriptive and predictive analytics initiatives where required.
• Translate complex technical and analytical findings into clear business recommendations.
• Present data-driven insights to technical and non-technical stakeholders.
• Collaborate with business teams to ensure analytics solutions address measurable business needs.
• Support iterative delivery of analytics capabilities within Agile environments.

• Collaboration & Stakeholder Engagement
• Partner with business stakeholders to understand requirements, data challenges, and desired outcomes.
• Translate business requirements into technical data solutions.
• Collaborate with data architects, developers, analysts, product managers, and other technology professionals.
• Participate in Agile ceremonies, planning sessions, technical discussions, and solution reviews.
• Communicate technical concepts clearly to both technical and non-technical audiences.
• Provide recommendations regarding data architecture, integration, quality, performance, and analytics.
• Contribute to knowledge sharing, documentation, mentoring, and continuous improvement.

• Required Skills & Experience
• Successful candidates should have a strong combination of data engineering, cloud, analytics, and enterprise technology experience.

Core Technical Skills:

• 7+ years of progressive experience in data engineering, data development, analytics engineering, or a closely related discipline.
• Strong experience designing and developing enterprise-scale data pipelines.
• Hands-on experience with cloud data platforms, preferably Azure.
• Strong experience with Azure Data Factory and/or modern cloud-based orchestration technologies.
• Experience with Databricks, Spark, PySpark, SQL, and notebook-based data processing.
• Strong SQL and data transformation skills.
• Experience developing ETL/ELT pipelines and data integration solutions.
• Experience with relational databases and enterprise data warehouses.
• Experience with data modeling, including dimensional modeling and fact/dimension design.
• Experience with data lakes, lakehouses, or modern cloud data architectures.
• Experience with Power BI and DAX for analytics and reporting.
• Experience with CI/CD, source control, automated testing, and deployment practices.
• Strong understanding of data quality, governance, metadata, security, and access management.

• Nice-to-Have Experience
• Microsoft Fabric
• Azure Synapse Analytics
• AWS data services
• Google Cloud Platform / BigQuery
• SSIS
• Azure Dataflows
• Python-based analytics or machine learning
• R
• NoSQL databases
• API-based data integration
• Data catalog and metadata platforms
• AI-assisted data engineering and analytics
• Experience supporting large enterprise or public-sector environments
• Experience working within Agile product teams

What We Are Looking For :

• We are looking for candidates who can operate beyond simply developing pipelines. The ideal Senior Data Engineer will be able to:
• Understand the business problem behind the data requirement.
• Design practical and scalable technical solutions.
• Work independently while collaborating effectively with multidisciplinary teams.
• Troubleshoot complex data and integration challenges.
• Communicate technical concepts clearly.
• Balance data quality, performance, security, and delivery timelines.
• Contribute to modern data architecture and engineering practices.
• Support analytics teams through reliable, well-modeled, accessible datasets.

Contract & Work Arrangement :

• The positions are primarily remote within Canada. Candidates should be comfortable working independently in a remote environment and collaborating virtually with distributed project teams.
• Occasional travel to Edmonton, Alberta may be required for meetings, workshops, or working sessions based on project needs and reasonable advance notice.
• Candidates must have their own equipment and a suitable remote-work environment. The working environment must support secure remote access and modern enterprise collaboration tools.

• Submission Requirements
• To be considered, candidates should provide:
• Current Resume
• Three Professional References
• References should be individuals familiar with comparable work performed by the candidate.
• Most recent/relevant reference should be listed first.
• Relevant Project Examples
• Include examples demonstrating experience with enterprise data engineering, cloud platforms, data pipelines, analytics, data integration, or modernization initiatives.

How to Apply :

Qualified Senior Data Engineers interested in this opportunity are encouraged to apply to the job posting directly or send their resume to [email protected] for immediate consideration.