special posting - sr. data engineer.

We are seeking a highly accomplished and technical Senior Data Engineer for an enterprise-level contract opportunity based in Toronto. In this role, you will take on a premier engineering capacity within the enterprise data and analytics streams, specializing in the architectural design, modeling, and construction of modern cloud data warehouses, data lakes, and business intelligence pipelines.

As a principal data authority, you will bridge the gap between fragmented raw operational data systems and analytics-ready data products. Operating within a hybrid framework, you will engineer scalable ETL/ELT pipelines, manage complex data transformation mechanics, and build curated data layers. This position requires an expert who can confidently optimize big data environments, translate business metrics into technical data models, and implement strict data quality and governance standards to drive meaningful enterprise insights.

Location: Toronto, ON

Assignment Type: Hybrid (Minimum 2 days onsite per week, according to office policy)

Contract Duration: 36 months

Advantages

Modern Cloud Stack Mastery: Direct, hands-on engineering control across cutting-edge cloud architectures including Azure Synapse, Databricks, and Azure Data Factory.

Lakehouse Architecture Implementation: Own the end-to-end processing lifecycle, designing scalable data pipelines following multi-layered (Bronze/Silver/Gold) storage patterns.

Data Product Autonomy: Convert raw, distributed data structures into high-performance, analytics-ready data assets and executive-level Power BI reporting models.

Extended Multi-Year Runway: Secure a definitive 36-month contract with deep long-term stability in a premier technical environment.

Responsibilities
Pipeline Engineering & Lakehouse Development
Model, design, develop, and maintain secure enterprise data warehouses, analytical data marts, and multi-tier data lakes.

Design, build, and document robust Azure Data Factory (ADF) and SSIS integration packages to seamlessly ingest and consolidate data from multiple operational systems.

Develop and maintain optimized Azure Databricks notebooks and data jobs using Python (PySpark) and SQL to run both large batch and streaming data workloads.

Build curated, analytics-ready data layers utilizing Delta Lake best practices and scalable data product engineering principles.

Author, refine, and optimize complex T-SQL code blocks, database stored procedures, user-defined views, triggers, and precise data indexes.

Data Modeling, Quality & Analytics
Support the conceptual, logical, and physical data modeling phases for enterprise data warehouse, data lake, and semantic layers.

Design and implement structural data quality frameworks, encompassing data cleansing rules, automated anomaly validations, and data stewardship workflows.

Conduct advanced data profiling and quantitative analysis to extract meaningful, data-driven patterns and operational insights.

Collaborate closely with business analysts and domain specialists to capture non-technical requirements and translate them into rigorous technical specifications.

DevOps, Governance & Enablement
Build, maintain, and optimize secure infrastructure and application deployment flows using CI/CD pipelines within Azure DevOps.

Formulate accurate project time estimates and execution plans for upcoming features, tasks, and data engineering sprints.

Compile and maintain highly organized technical documentation, functional design blueprints, and clear pipeline metadata records.

Support downstream QA, System Integration, and User Acceptance Testing (UAT) phases, delivering rapid environment troubleshooting and defect mitigation.

Research emerging cloud data trends and lead structural knowledge-transfer sessions to ensure long-term team sustainability.

Qualifications
Core Technical & Data Engineering Experience
Professional Data Tenure: 5+ years of progressive, hands-on experience in database development or data engineering for large enterprise-level environments.

Azure Data Stack Depth: Extensive technical experience designing, building, and configuring data ecosystems utilizing Microsoft Azure Synapse, Azure Data Factory, and Azure Databricks.

Relational Database Expertise: Deep operational mastery managing, configuring, and tuning SQL Server 2016 (or later) environments and SQL Data Warehouses.

Programming & Scripting Fluency: High proficiency in advanced SQL (T-SQL) and Python (including PySpark) for data manipulation and big data pipeline creation.

DevOps Pipeline Automation: Direct experience deploying and managing continuous delivery models through Azure DevOps CI/CD features.

Data Visualization Tooling: Solid track record building high-performance semantic models and executive dashboards using Microsoft Power BI or comparable analytics engines.

Frameworks & Architecture Capabilities
Analytical Design Architecture: Expert understanding and enterprise-scale experience implementing Data Warehouse (DW) structures, business intelligence (BI) systems, and multi-layered data lake models.

Data Governance Fluency: Advanced background setting up automated data quality monitoring, data cleansing algorithms, and master data stewardship protocols.

Analytical Problem Solving: Superior analytical reasoning and troubleshooting capabilities to resolve complex data ingestion blocks and evaluate edge-case system logic constraints.

Education & Compliance Attributes
Education: University degree in Computer Science, Software Engineering, or a related computer/business discipline, or an approved equivalent combination of verified education and hands-on experience.

Inclusion Standards Awareness: Foundational understanding of digital accessibility standards and universal design regulations (AODA and Ontario Human Rights Code implications as they relate to data interfaces and technology platforms).

Communication & Collaboration: Outstanding verbal and written communication skills, with polished ease when conveying technical data designs to both technical squads and business partners.

Summary
If you're interested in the "Senior Data Engineer" role based in Toronto, we encourage you to apply online at 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 Voir email sur randstad.com to ensure their ability to fully participate in the interview process.

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