Senior Data Engineer modernizing Alberta environmental regulatory data on Microsoft Azure. Building governed pipelines, APIs, and analytics-ready datasets for the DRAS platform.
Responsibilities
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 in cloud-native and hybrid environments
Lead development of data ingestion, transformation, and integration pipelines using Azure Data Factory, Azure Databricks, and Azure Synapse Analytics
Manage data lakes and structured storage solutions using Azure Data Lake Storage Gen2 with the Data Architect
Integrate diverse source systems, including ServiceNow and geospatial systems, using APIs, connectors, and custom scripts
Develop and maintain data models and semantic layers for operational reporting, analytics, machine learning, and downstream consumption
Build and optimize Python and SQL workflows for data cleansing, enrichment, and advanced analytics in Azure Databricks
Design and expose secure data services and APIs using Azure API Management
Implement metadata management, data classification, and lineage tracking
Ensure privacy and regulatory compliance through role-based access controls, encryption, and data masking
Monitor and troubleshoot data pipelines and integrations for reliability, scalability, and performance
Use AI and automation tools for pipeline development, testing, monitoring, and documentation
Leverage AI-assisted tools for code generation, optimization, and review
Design and curate standardized, high-quality datasets for advanced analytics and future AI use cases
Perform other duties as needed
Requirements
Experience designing scalable, secure, and high-performance data architecture on Microsoft Azure
Experience with Azure Data Factory, Azure Databricks, and Azure Synapse Analytics
Experience managing Azure Data Lake Storage Gen2
Experience integrating ServiceNow and geospatial systems using APIs, connectors, and custom scripts
Proficiency in Python and SQL
Knowledge of data modeling and semantic layers
Knowledge of Azure API Management
Knowledge of metadata management, data classification, and data lineage
Knowledge of role-based access controls, encryption, and data masking
Knowledge of FOIP and GDPR compliance
Ability to use AI and automation tools for data engineering workflows
Ability to use AI-assisted tools for code generation, optimization, and code review
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