SENIOR DATA ENGINEER (10 Resources Required) Location: Remote (within Canada) Engagement Type: Contract Rate (CAD): Up to $115.00 per hour / Commensurate with related experience and market competitiveness Term: 2026-09-14 to 2027-09-30 with two 12-month extensions available (up to 36-month contract) Standard (Criminal Record Check) Tri-global Solutions Group Inc. is seeking ten (10) Senior Data Engineers to join our talented Service Delivery team at Digital Design and Delivery (a division of Government of Alberta) to support projects across various ministries.

WORK MODEL: The successful candidate(s) will work remotely; however, may be required to attend meetings or work sessions in Edmonton, AB on reasonable notice provided by the Client (onsite attendance unlikely for out-of-province contractors). It is anticipated the role will 100% remote. Work must be performed from within Canada, due to network and data security policies. Applicants must be authorized to work in Canada to apply (Canadian Citizen or Permanent Resident).

Standard

Hours of work are between 08:15- 16:30 Mountain Time, Monday through Friday excluding observed holidays. ———————————————————————— PROJECT OVERVIEW The Digital Design and Delivery (DDD) division serves as the GoA’s center for modern digital delivery, partnering with ministries to design and deliver digital products, platforms, and services. DDD applies human-centered design, agile delivery, modern data practices, and AI-enabled approaches to improve service outcomes and advance digital transformation across government. Working within multidisciplinary product teams, Data Engineer(s) will collaborate with business and technical stakeholders to understand data requirements and develop modern data solutions.

The ideal candidate will have a strong foundation in data engineering practices, combined with the analytical skills necessary to derive actionable insights from complex datasets. The role supports the delivery of data solutions, including data pipelines, integration and migration capabilities, data models, analytics, reporting, and data governance practices. By combining technical expertise with analytical insight, Data Engineer(s) enable ministries to improve data quality and accessibility, strengthen self-service analytics, and make informed decisions that support the delivery of modern digital services across the Government of Alberta The Data Engineer(s) will be required on a full-time basis, working across two (2) to three (3) projects.

Time, location and frequency of work will vary depending on the needs of the project. Services and project deliverables should evolve as the work progresses in response to emerging user and business needs, as well as evolving design and technical opportunities. However, the following deliverables must be delivered iteratively throughout the course of the project: Data Engineering Design, build,



and maintain scalable data pipelines across on-premises and cloud platforms (Azure, Databricks, Microsoft Fabric, GCP, AWS) to ingest, transform, and store diverse datasets in support of enterprise business use cases.

Develop, optimize, and maintain data models, including dimensional models (star and snowflake schemas), to improve query performance, scalability, and usability for analytics and reporting. Integrate data from a variety of sources, including relational databases, NoSQL platforms, APIs, and files, applying AI-enabled data integration techniques such as intelligent data mapping, schema discovery, metadata enrichment, and automated data quality validation to improve accuracy and efficiency. Enhance ETL/ELT processes through optimization, automation, and performance tuning to improve scalability, reduce bottlenecks, and support high-volume data processing.

Develop and operate end-to-end ETL/ELT workflows using tools such as SSIS, Azure/Fabric Data Factory, Dataflows, and Notebooks, incorporating data validation, error handling, logging, monitoring, and scheduling to ensure reliable data operations. Automate data pipeline deployment and operations through CI/CD practices, including automated testing, release management, and monitoring to enable faster and more reliable delivery. Support the management and governance of enterprise data platforms, including data lakes, data warehouses, security controls, and access management.

Partner with architects, developers, and stakeholders to translate requirements into solutions, and prepare curated data marts and fact/dimension tables to support analytics.

Data Analytics

Use statistical methods, DAX, Python, and R to generate insights that inform business strategies. Develop interactive Power BI dashboards and reports, leveraging DAX to create calculated columns and measures, monitor key performance indicators, deliver service dashboards, and communicate results effectively to stakeholders. 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. Provide data-driven analysis, visualizations, and AI-enabled insights to support corporate priorities, strategic initiatives, and informed decision-making. Bachelor degree in Computer Science, Information Technology, Engineering, Mathematics, Statistics,



Data Science, or a related field. (Business Intelligence and Executive Reporting: Built executive dashboards, KPI reporting, self-service BI solutions and business performance reporting. (Cloud or Hybrid Data Platforms: Experience with Cloud modernization, or hybrid/cloud data platform implementations. (Data Migration and Modernization: Plan, execute, validate, and support data migrations across on-premises, cloud, and cross-database environments. (Data Warehouse and Lakehouse Design: Enterprise data warehouse or lakehouse projects using Star/Snowflake schemas, fact/dimension modeling. (Experience as a Data Engineer and/or Data Analyst. (Experience in Python (including PySpark) and SQL, applied to developing, orchestrating, and optimizing enterprise-grade ETL/ELT workflows in a large-scale cloud environment. (Knowledge of ETL processes and tools, with hands‑on experience designing and implementing data pipelines for transforming and loading data from multiple sources into data warehouses. (Experience in AI-Assisted Development Tools and Practices: Leverage AI-assisted development tools to improve productivity, code quality, documentation, testing, and data engineering workflows while applying appropriate review and quality controls. (Experience in DevOps, CI/CD, and Infrastructure as Code: Designing, implementing, or maintaining CI/CD pipelines and Infrastructure as Code practices to support automated deployment, configuration, and management of cloud-based data platforms and services. (Contemporary data technology: Experience in contemporary data technologies such as Microsoft Fabric, Databricks, Spark, Delta Lake, or similar lakehouse and big data platforms. (The proposed resource must have experience supporting enterprise-scale for public service applications in public sector, or mixed delivery environments. (PROJECT EXAMPLES (MUST PROVIDE 2 PROJECT/ASSIGNMENT EXAMPLES) # Provide an overview of a project or assignment that demonstrates the proposed resource’s data engineering experience.

Describe the business or data problem being addressed, the project scope, and the proposed resource’s specific responsibilities, contributions, and outcomes. # Describe a project where the proposed resource designed, built, and maintained data pipelines within a cloud-based or hybrid data platform. Azure, Microsoft Fabric, AWS, or GCP), data sources, ETL/ELT processes, and architecture components. Explain how reliability, scalability, performance, security, and access management requirements were addressed. # Describe a project where the proposed resource developed data models and integrated data from multiple sources.

Include dimensional models (star or snowflake schemas), APIs, relational databases, files, or NoSQL sources. Explain how data quality, validation, metadata management, and governance requirements were incorporated into the solution. # Describe a project #

📌 Senior Data Engineer (x10) (Edmonton)
🏢 Tri-global Solutions Group
📍 Edmonton