Must Haves: - Bachelor degree in Computer Science, IT or related field of study.
- 3 years experience ensuring data quality, security, and governance.
- 5 years Experience as a Data Engineer and/or Data Analyst.
- 3 years Experience designing efficient dimensional models (star and snowflake schemas) for warehousing and analytics.
- 3 years Experience developing and maintaining reports, dashboards, and visualizations using Power BI, DAX, Tableau, or Python libraries.
- 5 years Experience manipulating and extracting data from diverse on-premises and cloud-based sources.
- 3 years Experience performing migrations across on-premises, cloud, and cross-database environments.
- 2 years Experience using Git, collaborative workflows, CI/CD pipelines, containerization (Docker/Kubernetes), and Infrastructure as Code (Terraform, ARM, CloudFormation) to deploy and migrate data solutions.
- 3 years Experience with SSIS, Azure Data Factory (ADF), and using APIs for extracting and integrating data across multiple platforms and applications.
Nice to Haves: - 2 years Experience in application development, with knowledge of object-oriented and functional programming/scripting languages.
- 1 years Experience in the Government of Alberta environment or an environment of equivalent size and complexity.
- 2 years Experience with databases and data integration, including PostgreSQL, MongoDB, Azure Cosmos DB and data intefration tools like Synapse pipeline, Fabric data factory, Informatica, Talend, DBT and Airbyte.
- 1 years Exposure to AI/ML tools and workflows relevant to data engineering, such as integrating AI-driven analytics or automation within cloud platforms like Databricks and Azure.
Responsibilities: • Design, build, and maintain data pipelines on-premises and in the cloud (Azure, GCP, AWS) to ingest, transform, and store large datasets. Ensure pipelines are reliable and support multiple business use cases.
• Create and optimize dimensional models (star/snowflake) to improve query performance and reporting. Ensure models are consistent, scalable, and easy for analysts to use.
• Integrate data from SQL, NoSQL, APIs, and files while maintaining accuracy and completeness. Apply validation checks and monitoring to ensure high-quality data.
• Improve ETL/ELT processes for efficiency and scalability. Redesign workflows to remove bottlenecks and handle large, disconnected datasets.
• Build and maintain end-to-end ETL/ELT pipelines with SSIS and Azure Data Factory. Implement error handling, logging, and scheduling for dependable operations.
• Automate deployment, testing, and monitoring of ETL workflows through CI/CD pipelines. Integrate releases into regular deployment cycles for faster, safer updates.
• Manage data lakes and warehouses with proper governance. Apply security best practices, including access controls and encryption.
• Partner with engineers, analysts, and stakeholders to translate requirements into solutions. Prepare curated data marts and fact/dimension tables to support self-service analytics.
Data Analytics:
• Analyze datasets to identify trends, patterns, and anomalies. Use statistical methods, DAX, Python, and R to generate insights that inform business strategies.
• Develop interactive dashboards and reports in Power BI using DAX for calculated columns and measures. Track key performance metrics, share service dashboards, and present results effectively.
• Build predictive or descriptive models using statistical, Python, or R-based machine learning methods. Design and integrate data models to improve service delivery.
• Present findings to non-technical audiences in clear, actionable terms. Translate complex data into business-focused insights and recommendations.
• Deliver analytics solutions iteratively in an Agile environment. Mentor teams to enhance analytics fluency and support self-service capabilities.
• Provide data-driven evidence to guide corporate priorities. Ensure strategies and initiatives are backed by strong analysis, visualizations, and models.