Senior Data Engineer to design, implement, and maintain robust data pipelines and architectures, as well as perform detailed data analysis to support business decisions (672)

Our client is seeking a Senior Data Engineer to design, implement, and maintain robust data pipelines and architectures, as well as perform detailed data analysis to support business decisions

Some travel/onsite work within Alberta may be required to conduct field research and user interviews.

Must Haves:

5+ years as a Data Analyst, Data Engineer or in a similar role. 5+ years of experience manipulating and extracting data from diverse on-premises and cloud-based sources. 3+ years ensuring data quality, security, and governance. 3+ years designing efficient dimensional models (star and snowflake schemas) for warehousing and analytics 2+ years using Git, collaborative workflows, CI/CD pipelines, containerization (Docker/Kubernetes), and Infrastructure as Code (Terraform, ARM, CloudFormation) to deploy and migrate data solutions. 3+ years with SSIS, Azure Data Factory (ADF), and using APIs for extracting and integrating data across multiple platforms and applications. 2+ years performing migrations across on-premises, cloud, and cross-database environments. Bachelor degree in Computer Science, IT or related field of study.

A combination of the following experience is also required:

2+ years of experience in application development, with working knowledge of modern technologies including Next.js, Node.js, D3.js, GitHub Actions, and Build Master automation. 2+ years of experience with databases and data integration, including PostgreSQL, MongoDB, Azure Cosmos DB, Azure Synapse, and Talend. 1+ years exposure to AI/ML tools and workflows relevant to data engineering, such as integrating AI-driven analytics or automation within cloud platforms like Databricks and Azure.

About the Role:

The Data Engineer will be required on a full-time basis, working across two to three projects. Time, location and frequency of work will vary depending on the needs of the particular project.

Services and project deliverables should evolve as the work progresses, in response to emerging user and business needs, as well as design and technical opportunities. However, the following must be delivered (iteratively) over the course of the project:

Data Engineering:

• Design, build, and maintain data pipelines on-premises and in the cloud (Azure, GCP, AWS) to ingest, transform, and store large datasets. Ensure pipelines are reliable and support multiple business use cases.

• Create and optimize dimensional models (star/snowflake) to improve query performance and reporting. Ensure models are consistent, scalable, and easy for analysts to use.

• Integrate data from SQL, NoSQL, APIs, and files while maintaining accuracy and completeness. Apply validation checks and monitoring to ensure high-quality data.

• Improve ETL/ELT processes for efficiency and scalability. Redesign workflows to remove bottlenecks and handle large, disconnected datasets.

• Build and maintain end-to-end ETL/ELT pipelines with SSIS and Azure Data Factory. Implement error handling, logging, and scheduling for dependable operations.

• Automate deployment, testing, and monitoring of ETL workflows through CI/CD pipelines. Integrate releases into regular deployment cycles for faster, safer updates.

• Manage data lakes and warehouses with proper governance. Apply security best practices, including access controls and encryption.

• Partner with engineers, analysts, and stakeholders to translate requirements into solutions. Prepare curated data marts and fact/dimension tables to support self-service analytics.

Data Analytics:

• Analyze datasets to identify trends, patterns, and anomalies. Use statistical methods, DAX, Python, and R to generate insights that inform business strategies.

• Develop interactive dashboards and reports in Power BI using DAX for calculated columns and measures. Track key performance metrics, share service dashboards, and present results effectively.

• Build predictive or descriptive models using statistical, Python, or R-based machine learning methods. Design and integrate data models to improve service delivery.

• Present findings to non-technical audiences in clear, actionable terms. Translate complex data into business-focused insights and recommendations.

• Deliver analytics solutions iteratively in an Agile environment. Mentor teams to enhance analytics fluency and support self-service capabilities.

• Provide data-driven evidence to guide corporate priorities. Ensure strategies and initiatives are backed by strong analysis, visualizations, and models.