Job Title: GOAPRDJP00000975 - Data Engineer - Intermediate

If you have the required experience and are available for new opportunities, please send the following documents to

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or View email address on ca.workus.org by Monday , July 27, 2026 at 4 :00 PM EST Below you can find some brief information.

• Updated Resume in word format (Mandatory)
• Skills Matrix and References (Mandatory)
• Expected hourly rate (Mandatory)
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Job Title: GOAPRDJP00000975 - Data Engineer - Intermediate

Client: Government of Alberta

Work Location : Eleventh Floor, 9942 - 108 Street, Edmonton, Alberta, CAN, T5K 2J5,

Remote

Estimated Start Date : 04/08/2026

Estimated End Date: 31/03/2027

#Business Days : 165.00

Estimated Hours per Day: 7.25

Maximum Extension Term: 6 Months

Must Have

• Use of AI – Experienced in using AI for code generation, data analysis, automation, and enhancing productivity in data engineering workflows – 1 year
• Experience building scalable data pipelines with Azure Databricks, Delta Lake, Workflows, Jobs, and Notebooks, plus cluster management. Extending solutions to Synapse Analytics and Microsoft Fabric is a plus – 3 years
• Experience designing data solutions for analytics-ready, trusted datasets using tools like Power BI and Synapse, including semantic layers, data marts, and data products for self-service, data science, and reporting – 3 years
• Experience in data governance, security, and metadata management within a Databricks-based platform – 2 years
• Experience in GitHub/Git for version control, collaborative development, code management, and integration with data engineering workflows – 4 years
• Experience with Azure services (Storage, SQL, Synapse, networking) for scalable, secure solutions, and with authentication (Service Principals, Managed Identities) for secure access in pipelines and integrations – 3 years
• Experience in Python (including Py Spark) and SQL, applied to developing, orchestrating, and optimizing enterprise-grade ETL/ELT workflows in a large-scale cloud environment – 5 years

Nice Have

• Direct, hands-on experience performing business requirement analysis related to data manipulation/transformation, cleansing and wrangling – 6 years
• Experience and strong technical knowledge of Microsoft SQL Server, including database design, optimization, and administration in enterprise environments – 6 years
• Experience extending or integrating data solutions with Azure Synapse Analytics and Microsoft Fabric (Lakehouse, Warehouse, Semantic Models) – 2 years
• Direct experience building data products in Government of Alberta cloud environment – 1 year
• Experience building scalable ETL pipelines, data quality enforcement, and cloud integration using TALEND technologies – 2 years
• Skilled in building secure, scalable RESTful APIs for data exchange, with robust auth, error handling, and support for real-time automation – 3 years
• Experience working with cross-functional teams to create software applications and data products – 5 years
• Experience working with ServiceNow – Azure based Data Management Platform Integrations – 1 year
• Experience in Message Queueing Technologies, implementing message queuing using tools like ActiveMQ and Service Bus for scalable, asynchronous communication across distributed systems – 3 years

Scope

Modernization initiatives across the Government of Alberta are fundamentally changing how ministry users collect, manage, analyse, and use data as legacy systems are transformed into modern Data Management and Geospatial Platforms. This shift requires dedicated analytical capacity to ensure that the value of modernized data assets is fully realized.

DRAS is a Government of Alberta regulatory transformation initiative led by Environment and Protected Areas (EPA) to modernize, digitize, and streamline environmental and natural resource regulatory processes. DRAS supports the full regulatory lifecycle, from application and authorization to monitoring, compliance, remediation, and closure through a single, consolidated digital platform

As DRAS development continues, the volume, variety, and complexity of structured data continue to grow, creating a sustained need for dedicated data engineering and data product expertise. The Data Product Analyst role is critical to ensuring that modernization delivers tangible business value. This role will design, build, and operate reliable data pipelines that ingest and integrate data into the DMP, apply standardized transformations, enforce data quality and governance controls, and produce trusted, analytics‑ready datasets that support regulatory oversight, compliance monitoring, and evidence‑based decision‑making aligned with DRAS objectives.

This position will primarily support the Digital Regulatory Assurance System (DRAS) program, where high quality, timely analytics are essential to regulatory and compliance functions. As data and analytics maturity increases, the role may be expanded to support additional enterprise data initiatives.

Duties

• Design and implement scalable, secure, and high-performance data architecture on Microsoft Azure, supporting both cloud-native and hybrid environments.
• Lead the development of data ingestion, transformation, and integration pipelines using Azure Data Factory, Azure Databricks, and Azure Synapse Analytics.
• Work with the Data Architect and manage data lakes and structured storage solutions using Azure Data Lake Storage Gen2, ensuring efficient access and governance.
• Integrate data from diverse source systems including ServiceNow, and geospatial systems, using APIs, connectors, and custom scripts.
• Develop and maintain robust data models and semantic layers to support operational reporting, analytics, and machine learning use cases.
• Build and optimize data workflows using Python and SQL for data cleansing, enrichment, and advanced analytics within Azure Databricks.
• Design and expose secure data services and APIs using Azure API Management for downstream systems.
• Implement data governance practices, including metadata management, data classification, and lineage tracking.
• Ensure compliance with privacy and regulatory standards (e.g., FOIP, GDPR) through role-based access controls, encryption, and data masking.
• Monitor and troubleshoot data pipelines and integrations, ensuring reliability, scalability, and performance across the platform.
• Utilize AI and automation tools to streamline data engineering workflows, including pipeline development, testing, monitoring, and documentation.
• Leverage AI-assisted tools for code generation, optimization, and review to improve development efficiency and code quality.
• Design and curate standardized, high‑quality datasets that are suitable for advanced analytics and future AI use cases.

Note

The resource will primarily work

Remotely but must be available for on-site meetings as required.