As a Data Engineer, you will be the foundational backbone of our data analytics delivery stream. You will design and implement high-performance data pipelines capable of handling both real-time streaming and batch processing. Beyond writing complex ETL/ELT logic and tuning SQL queries for maximum efficiency, you will collaborate with Data Scientists, ML Developers, and Cloud Architects to evolve our data platform environment. This role is perfect for a developer with a data-backend focus who champions clean code, automated data quality tracking, and scalable cloud architecture.
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Advantages
End-to-End Architectural Impact: Shape the future of the enterprise data platform by participating directly in conceptual, logical, and physical data modeling.
Hybrid Data & ML Scope: Gain highly sought-after MLOps experience by helping operationalize production-grade machine learning pipelines.
Modern Tech Stack: Deepen your cloud expertise with advanced AWS infrastructure and get early exposure to emerging platforms like Microsoft Fabric.
Engineering-First Culture: Work in an environment that prioritizes strong software fundamentals, robust testing processes, automated deployments, and constructive peer code reviews.
Responsibilities
1. Pipeline Architecture & Infrastructure Engineering
Build, improve, and maintain data infrastructure for ingesting, storing, and transforming data across advanced analytical workflows.
Implement robust, scalable data pipelines supporting both stream and batch processing.
Perform end-to-end ETL/ELT processes to extract and load data from a wide variety of structured and unstructured sources.
Partner with enterprise architects and Information Services (IS) teams to design and evolve the overarching data platform environment in AWS.
2. Data Manipulation & Query Optimization
Write complex, automated SQL queries to manipulate data extracts and manage large-scale data flows.
Perform continuous query optimization, performance analysis, and database tuning to minimize processing latency and cost.
Assist with logical and physical data modeling, implementing optimized data structures (e.g., star and snowflake schemas).
3. Quality Assurance, DevOps & MLOps
Automate cloud deployments, data flows, and automated data quality validation checks.
Collaborate closely with Data Scientists and ML developers to build, test, and operationalize automated ML pipelines.
Design rigorous testing processes, formulating and executing comprehensive test cases for data validation.
Conduct thorough code reviews, troubleshoot system defects, and provide constructive feedback to elevate code quality across the distributed engineering team.
Qualifications
Core Requirements
Experience: Minimum of 3+ years in a dedicated Data Engineering role or a Software Developer role with a strong focus on data-backend development and complex transformations.
Programming Depth: Excellent, production-level knowledge of Python.
Database & Query Mastery: Strong knowledge of SQL paired with a deep understanding of performance analysis and query optimization techniques.
Software Engineering Foundations: Rigid grasp of computer science fundamentals, including modularity, abstraction, data structures, and algorithms.
Data Modeling: Solid understanding of core data modeling concepts (normalized vs. denormalized data architectures, conceptual/logical/physical models, and star/snowflake schemas).
Technical Infrastructure & Cloud Skills
Proven experience building automated, production-grade ETL/ELT pipelines.
Solid project experience leveraging various data storage technologies (including RDBMS, NoSQL, and Graph Databases).
Direct experience with cloud infrastructure provisioning and deployment automation on AWS.
Preferred Qualifications (Nice to Have)
Education: BSc. degree in Computer Science, Engineering, Mathematics, Physics, Statistics, or an equivalent quantitative field.
Advanced Platforms: In-depth knowledge of the broader AWS ecosystem combined with exposure to Microsoft Fabric.
Process: Experience working within fast-paced Agile development methodologies.
Summary
Our Client is seeking a highly skilled Data Engineer to take ownership of end-to-end analytical system design and cloud data architecture. In this role, you will build, scale, and optimize the infrastructure required for ingesting, storing, and transforming massive datasets to fuel advanced analytical workflows and machine learning models. This position balances pure software engineering rigor with advanced data platform management within an AWS ecosystem.
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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As a Data Engineer, you will be the foundational backbone of our data analytics delivery stream. You will design and implement high-performance data pipelines capable of handling both real-time streaming and batch processing. Beyond writing complex ETL/ELT logic and tuning SQL queries for maximum efficiency, you will collaborate with Data Scientists, ML Developers, and Cloud Architects to evolve our data platform environment. This role is perfect for a developer with a data-backend focus who champions clean code, automated data quality tracking, and scalable cloud architecture.
Advantages
End-to-End Architectural Impact: Shape the future of the enterprise data platform by participating directly in conceptual, logical, and physical data modeling.
Hybrid Data & ML Scope: Gain highly sought-after MLOps experience by helping operationalize production-grade machine learning pipelines.
Modern Tech Stack: Deepen your cloud expertise with advanced AWS infrastructure and get early exposure to emerging platforms like Microsoft Fabric.
Engineering-First Culture: Work in an environment that prioritizes strong software fundamentals, robust testing processes, automated deployments, and constructive peer code reviews.
...
Responsibilities
1. Pipeline Architecture & Infrastructure Engineering
Build, improve, and maintain data infrastructure for ingesting, storing, and transforming data across advanced analytical workflows.
Implement robust, scalable data pipelines supporting both stream and batch processing.
Perform end-to-end ETL/ELT processes to extract and load data from a wide variety of structured and unstructured sources.
Partner with enterprise architects and Information Services (IS) teams to design and evolve the overarching data platform environment in AWS.
2. Data Manipulation & Query Optimization
Write complex, automated SQL queries to manipulate data extracts and manage large-scale data flows.
Perform continuous query optimization, performance analysis, and database tuning to minimize processing latency and cost.
Assist with logical and physical data modeling, implementing optimized data structures (e.g., star and snowflake schemas).
3. Quality Assurance, DevOps & MLOps
Automate cloud deployments, data flows, and automated data quality validation checks.
Collaborate closely with Data Scientists and ML developers to build, test, and operationalize automated ML pipelines.
Design rigorous testing processes, formulating and executing comprehensive test cases for data validation.
Conduct thorough code reviews, troubleshoot system defects, and provide constructive feedback to elevate code quality across the distributed engineering team.
Qualifications
Core Requirements
Experience: Minimum of 3+ years in a dedicated Data Engineering role or a Software Developer role with a strong focus on data-backend development and complex transformations.
Programming Depth: Excellent, production-level knowledge of Python.
Database & Query Mastery: Strong knowledge of SQL paired with a deep understanding of performance analysis and query optimization techniques.
Software Engineering Foundations: Rigid grasp of computer science fundamentals, including modularity, abstraction, data structures, and algorithms.
Data Modeling: Solid understanding of core data modeling concepts (normalized vs. denormalized data architectures, conceptual/logical/physical models, and star/snowflake schemas).
Technical Infrastructure & Cloud Skills
Proven experience building automated, production-grade ETL/ELT pipelines.
Solid project experience leveraging various data storage technologies (including RDBMS, NoSQL, and Graph Databases).
Direct experience with cloud infrastructure provisioning and deployment automation on AWS.
Preferred Qualifications (Nice to Have)
Education: BSc. degree in Computer Science, Engineering, Mathematics, Physics, Statistics, or an equivalent quantitative field.
Advanced Platforms: In-depth knowledge of the broader AWS ecosystem combined with exposure to Microsoft Fabric.
Process: Experience working within fast-paced Agile development methodologies.
Summary
Our Client is seeking a highly skilled Data Engineer to take ownership of end-to-end analytical system design and cloud data architecture. In this role, you will build, scale, and optimize the infrastructure required for ingesting, storing, and transforming massive datasets to fuel advanced analytical workflows and machine learning models. This position balances pure software engineering rigor with advanced data platform management within an AWS ecosystem.
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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