数据工程师 - 高级 JP989

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

Originally posted on Himalayas