Job Title: Data Engineer - Senior

Duration: 12 months (hours: 1,805.25) (Maximum Extension Term: 6 months)

Location: Edmonton, AB (9942 - 108 ST, Edmonton, Alberta T5K 2J5)

Hours/Day: 7.25

Hours/Week: 36.25

Notes on Location:

- The resource will primarily work remotely but must be available for on-site meetings as required. These meetings may involve strategic, analytical, or technical discussions and may include engagement with team members, senior managers, directors, executive directors, or business clients.
- On-Site Meeting Frequency:

Meetings may occur up to 3–4 times (or more) per fiscal month, but the actual frequency will depend on the specific initiative and will be determined on an on-demand basis.
- On-Site Location:

Meetings will take place in Edmonton, Alberta, at one of the Government of Alberta buildings.

The resource will be provided with details regarding the meeting location in advance.

- This policy ensures flexibility for remote work while maintaining the ability to collaborate effectively during key on-site engagements. However, time to travel and any associated expenses to and from Edmonton and/or travel within Alberta will be at no cost to the Province.

Description:

Project Name:

Digital Regulatory Assurance System

Scope:

Modernization initiatives across the Government of Alberta are fundamentally changing how ministry users collect, manage, analyze, 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

- Collaborate with business stakeholders and product owners to understand data product objectives, requirements, and success criteria
- 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 and downstream consumption.
- 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.
- Other duties as needed

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 of the proposed resource.

QUALIFICATIONS:

Must Have

Attestation

Yes/No - Attestation - Conflict of Interest

Work Experience

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

Experience using version control systems

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

Hands-on Experience in Python and SQL for Data Engineering

Hands-on Experience with Azure Databricks and Delta Lake

Use of AI-Experienced in using AI for code generation, data analysis, automation, and enhancing productivity in data engineering workflows.

Nice to Have

Work Experience

Direct, hands-on experience performing business requirement analysis related to data manipulation/transformation, cleansing and wrangling.

Experience and solid technical knowledge of Microsoft SQL Server, including database design, optimization, and administration in enterprise environments.

Experience and technical knowledge of Microsoft Fabric.

Experience building data products in Cloud Environment

Experience in Designing and Integrating RESTful APIs.

Experience in Message Queueing Technologies, implementing message queuing using tools like ActiveMQ and Service Bus for scalable, asynchronous communication across distributed systems.

Experience working with cross-functional teams to create software applications and data products.

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