Position Title: GOAPRDJP00000982 - Senior Data Engineer

Location: Edmonton, Alberta

Mode: Contract

Client: Government of Alberta

Position Overview The successful candidates will work within multidisciplinary product teams and collaborate with business, data, architecture, development, and technical stakeholders to design, build, and maintain up-to-date enterprise data solutions.

The role will focus on data engineering, data integration, data migration, data modeling, analytics, reporting, data governance, cloud data platforms, and data modernization. Candidates should be capable of transforming complex and diverse datasets into reliable, scalable, and accessible data solutions that support government services and informed decision-making.

Key Responsibilities

Data Engineering

- Design, build, optimize, and maintain scalable data pipelines across on-premises and cloud environments.
- Develop data solutions using cloud platforms and technologies such as Azure, Databricks, Microsoft Fabric, GCP, and AWS.
- Develop and maintain enterprise data models, including dimensional, star schema, and snowflake schema models.
- Design fact and dimension tables and curated data marts to support analytics and reporting requirements.
- Integrate data from relational databases, NoSQL platforms, APIs, files, and other enterprise data sources.
- Develop and optimize ETL/ELT pipelines for large-scale data processing.
- Implement data validation, error handling, logging, monitoring, scheduling, and operational controls within data workflows.
- Use technologies such as SSIS, Azure/Fabric Data Factory, Dataflows, and Notebooks to develop end-to-end data workflows.
- Optimize data pipelines to improve scalability, performance, reliability, and processing efficiency.
- Support data migration and modernization initiatives across on-premises, cloud, and cross-database environments.
- Implement automated testing, deployment, release management, and monitoring for data pipelines through CI/CD practices.
- Support enterprise data platforms, including data lakes, data warehouses, lakehouses, security controls, and access management.
- Collaborate with architects, developers, analysts,



product teams, and business stakeholders to translate requirements into scalable technical solutions.

Data Analytics & Business Intelligence

- Analyze complex datasets to identify trends, patterns, anomalies, and actionable insights.
- Develop business intelligence solutions, executive dashboards, KPI reporting, and self-service analytics capabilities.
- Design and develop interactive Power BI dashboards and reports.
- Use DAX to develop calculated columns, measures, KPIs, and analytical solutions.
- Apply Python, R, SQL, and statistical methods to analyze data and develop predictive or descriptive models.
- Apply machine learning and analytical techniques where appropriate to improve service delivery and decision-making.
- Translate complex technical and analytical findings into clear, actionable recommendations for non-technical stakeholders.
- Develop data visualizations and AI-enabled insights to support strategic initiatives and corporate priorities.
- Mentor teams and support the development of data and analytics self-service capabilities.
- Deliver analytics solutions iteratively within an Agile product development environment.

Data Governance & Quality

- Support enterprise data governance, data quality, metadata management, security, and access-control practices.
- Implement data quality validation and monitoring processes.
- Ensure data solutions comply with applicable government policies, security requirements, privacy standards, and organizational practices.
- Apply appropriate controls when using AI-assisted development and data engineering tools.
- Maintain technical documentation, data models, pipeline documentation, and operational procedures.

Required Qualifications

Education

- Bachelor's degree in Computer Science, Information Technology,



Engineering, Mathematics, Statistics, Data Science, or a related discipline.

Mandatory Experience

Candidates must demonstrate the following experience:

- 5+ years of experience working as a Data Engineer and/or Data Analyst.
- 5+ years of experience with Python, including PySpark, and SQL applied to developing, orchestrating, and optimizing enterprise-grade ETL/ELT workflows in large-scale cloud environments.
- 4+ years of experience with ETL processes and tools, including designing and implementing data pipelines that transform and load data from multiple sources into data warehouses.
- 4+ years of experience with data warehouse and/or lakehouse design, including star/snowflake schemas and fact/dimension modeling.
- 4+ years of experience with Business Intelligence and Executive Reporting, including executive dashboards, KPI reporting, self-service BI, and business performance reporting.
- 4+ years of experience with cloud or hybrid data platforms, including cloud modernization or hybrid/cloud data platform implementations.
- 2+ years of experience with data migration and modernization, including planning, executing, validating, and supporting migrations across on-premises, cloud, and cross-database environments.

Preferred Qualifications The following experience will be considered an asset:

- 6+ years of experience with AI-assisted development tools and practices, including using AI to improve productivity, code quality, documentation, testing, and data engineering workflows while maintaining appropriate review and quality controls.
- 6+ years of experience with DevOps, CI/CD, and Infrastructure as Code, including designing, implementing, or maintaining automated deployment pipelines and IaC practices for cloud-based data platforms.
- 2+ years of experience with modern data technologies, such as:
- Microsoft Fabric
- Databricks
- Apache Spark
- Delta Lake
- Lakehouse platforms
- Other modern big-data technologies
- 3+ years of experience supporting enterprise-scale public-sector applications or working in public-sector or mixed-delivery environments.

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