Senior Data Engineer building cloud and on-premises data pipelines for Alberta government services.
Developing Power BI analytics, data models, governance, and AI-enabled insights across digital transformation projects.
Responsibilities
Collaborate with business and technical stakeholders to understand data requirements and develop modern data solutions
Design, build, and maintain scalable data pipelines across on-premises and cloud platforms
Develop, optimize, and maintain dimensional and other data models for analytics and reporting
Integrate data from relational databases, NoSQL platforms, APIs, and files
Enhance ETL/ELT processes through optimization, automation, and performance tuning
Develop and operate end-to-end ETL/ELT workflows with validation, error handling, logging, monitoring, and scheduling
Automate data pipeline deployment and operations through CI/CD practices
Support governance of enterprise data platforms, data lakes, data warehouses, security controls, and access management
Prepare curated data marts and fact/dimension tables to support analytics
Analyze datasets to identify trends, patterns, and anomalies
Develop interactive Power BI dashboards and reports and monitor key performance indicators
Build predictive or descriptive models using statistical, Python, or R-based methods
Present findings to non-technical audiences and translate complex data into actionable recommendations
Deliver analytics solutions iteratively in an Agile environment
Mentor teams to enhance analytics fluency and support self-service capabilities
Provide data-driven analysis, visualizations, and AI-enabled insights for strategic decision-making
Requirements
Strong foundation in data engineering practices
Analytical skills to derive actionable insights from complex datasets
Experience designing, building, and maintaining scalable data pipelines
Knowledge of Azure, Databricks, Microsoft Fabric, GCP, and AWS
Experience with dimensional data models, including star and snowflake schemas
Experience integrating relational databases, NoSQL platforms, APIs, and files
Knowledge of AI-enabled data integration techniques, schema discovery, metadata enrichment, and automated data quality validation
Experience with ETL/ELT optimization, automation, and performance tuning
Experience with SSIS, Azure/Fabric Data Factory, Dataflows, and Notebooks
Knowledge of CI/CD, automated testing, release management, and monitoring
Knowledge of data lakes, data warehouses, security controls, and access management
Proficiency in DAX, Python, and R
Experience developing Power BI dashboards and reports
Experience building predictive or descriptive statistical or machine-learning models
Experience delivering solutions in an Agile environment
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