Senior Data Engineer

Please send your resume at [email protected] if you are interested in this 12 months (09/14/2026 to 09/30/2027) contract role. Please feel free to forward if you are not interested. This is with Govt of Alberta.

Data Engineer - Senior (GOAPRDJP00000982) - 10 Resources

Work Status: Remote; however, may be required to attend meetings or work sessions in Edmonton on reasonable notice from the Province

Work Location: 7000 - 113 ST, Edmonton, Alberta

• Must Have: Education
• Bachelor degree in Computer Science, Information Technology, Engineering, Mathematics, Statistics, Data Science, or a related field.

• Must Have: Work Experience
• 4 years business Intelligence and Executive Reporting: Built executive dashboards, KPI reporting, self-service BI solutions and business performance reporting.
• 4 years cloud or Hybrid Data Platforms: Experience with Cloud modernization, or hybrid/cloud data platform implementations.
• 2 years data Migration and Modernization: Plan, execute, validate, and support data migrations across on-premises, cloud, and cross-database environments.
• 4 years data Warehouse and Lakehouse Design: Enterprise data warehouse or lakehouse projects using Star/Snowflake schemas, fact/dimension modeling.
• 5 years experience as a Data Engineer and/or Data Analyst.
• 5 years experience in Python (including PySpark) and SQL, applied to developing, orchestrating, and optimizing enterprise-grade ETL/ELT workflows in a large-scale cloud environment.
• 4 years 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.

• Nice to Have: Work Experience
• 1 years 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.
• 2 years 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.
• 1 years experience in modern data technologies such as Microsoft Fabric, Databricks, Spark, Delta Lake, or similar lakehouse and big data platforms. 1 years years
• 2 years must have experience supporting enterprise-scale for public service applications in public sector, or mixed delivery environments.

• Other Mandatory Requirements
• Two (2) project examples must be provided for each proposed resource, which exemplify and demonstrate the proposed resource’s expertise in data engineering and analytics area. Project examples need to be added to the bottom of the resume. Questions 1 through 5 must be answered for each project example. The Evaluation Team must be able to determine which project any given answer relates to. Where the answer to a Question is the same for both projects, this must be clearly stated.
• 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. Include the cloud platform(s) used (e.g., 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 where the proposed resource delivered analytics, dashboards, reports, or business insights. Include the tools used (e.g., Power BI, DAX, Python, R), key metrics or outputs delivered, target audience, and how the results supported business decisions, operational improvements, or service outcomes.
• Provide a list of the data engineering, analytics, cloud, and reporting tools and technologies personally used by the proposed resource. For each technology, identify the years of experience and where it was applied.
• Project Overview:
• The Government of Alberta (GoA) has embarked on transforming the work of government to deliver simpler, more efficient, and better services for Albertans. 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

Scope of Services:

• 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. At the end of each term, it is expected that the Data Engineer(s) may work a maximum of 1,960 hours, unless otherwise agreed upon with the province. However, Data Engineer(s) may be required to work fewer or more hours depending on the nature and needs of their work, as directed by the province.
• 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

Location of Work:

• Data Engineer(s) will work remotely; however, may be required to attend meetings or work sessions in Edmonton on reasonable notice from the Province. At the time of providing such notice, the Province will advise of the expected duration of any such meetings or work sessions. However, time to travel and any associated expenses to and from Edmonton will be at no cost to the Province.
• The Province reserves the right to alter this work arrangement on reasonable notice to the Data Engineer(s). The Supplier and the Data Engineer(s) will be consulted about the alteration in work arrangement; however, the Province retains ultimate discretion as to the appropriate work arrangement.
• Some travel within Alberta may be required to conduct field research and user interviews. The Province will make arrangements for travel for field research and user interview purposes where possible at no cost to the province.
• Work must be done within Canada.

Evaluation:

• Qualifications- 20%
• Other Mandatory Requirements- 25%
• Interview - 50%
• Pricing - 5%
• SUBMISSION MUST INCLUDE:
• RESUME
• ALL REQUIRED EXPERIENCE MUST BE DESCRIBED IN RESUME UNDER THE JOB/PROJECT WHERE EXPERIENCE WAS ATTAINED.
• EACH JOB/PROJECT MUST CONTAIN THE TERM OF THE JOB/PROJECT IN THE FORMAT MMM/YYYY to MMM/YYYY.
• RESOURCE REFERENCES
• Three references, for whom similar work has been performed, must be provided. The most recent reference should be listed first. Reference checks may or may not be completed to assist with scoring the proposed resource.