Entry Level Software Engineer Machine Learning Jobs in Alberta

Project Overview:

The Government of Alberta (GoA) has embarked on transforming the work of government to deliver simpler, more efficient, and better services for Albertans. The Digital Design and Delivery (DDD) division serves as the GoA's center for modern digital delivery, partnering with ministries to design and deliver digital products, platforms, and services. DDD applies human-centered design, agile delivery, modern data practices, and AI-enabled approaches to improve service outcomes and advance digital transformation across government.

Working within multidisciplinary product teams, Data Engineer(s) will collaborate with business and technical stakeholders to understand data requirements and develop modern data solutions. The ideal candidate will have a strong foundation in data engineering practices, combined with the analytical skills necessary to derive actionable insights from complex datasets.

The role supports the delivery of data solutions, including data pipelines, integration and migration capabilities, data models, analytics, reporting, and data governance practices. By combining technical expertise with analytical insight, Data Engineer(s) enable ministries to improve data quality and accessibility, strengthen self-service analytics, and make informed decisions that support the delivery of modern digital services across the Government of Alberta

Scope of Services:

The Data Engineer(s) will be required on a full-time basis, working across two (2) to three (3) projects. Time, location and frequency of work will vary depending on the needs of the project. At the end of each term, it is expected that the Data Engineer(s) may work a maximum of 1,960 hours, unless otherwise agreed upon with the province. However, Data Engineer(s) may be required to work fewer or more hours depending on the nature and needs of their work, as directed by the province.

Services and project deliverables should evolve as the work progresses in response to emerging user and business needs, as well as evolving design and technical opportunities. However, the following deliverables must be delivered iteratively throughout the course of the project:

Data Engineering:

• Design, build, and maintain scalable data pipelines across on-premises and cloud platforms (Azure, Databricks, Microsoft Fabric, GCP, AWS) to ingest, transform, and store diverse datasets in support of enterprise business use cases.

• Develop, optimize, and maintain data models, including dimensional models (star and snowflake schemas), to improve query performance, scalability, and usability for analytics and reporting.

• Integrate data from a variety of sources, including relational databases, NoSQL platforms, APIs, and files, applying AI-enabled data integration techniques such as intelligent data mapping, schema discovery, metadata enrichment, and automated data quality validation to improve accuracy and efficiency.

• Enhance ETL/ELT processes through optimization, automation, and performance tuning to improve scalability, reduce bottlenecks, and support high-volume data processing.

• Develop and operate end-to-end ETL/ELT workflows using tools such as SSIS, Azure/Fabric Data Factory, Dataflows, and Notebooks, incorporating data validation, error handling, logging, monitoring, and scheduling to ensure reliable data operations.

• Automate data pipeline deployment and operations through CI/CD practices, including automated testing, release management, and monitoring to enable faster and more reliable delivery.

• Support the management and governance of enterprise data platforms, including data lakes, data warehouses, security controls, and access management.

• Partner with architects, developers, and stakeholders to translate requirements into solutions, and prepare curated data marts and fact/dimension tables to support 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 Power BI dashboards and reports, leveraging DAX to create calculated columns and measures, monitor key performance indicators, deliver service dashboards, and communicate results effectively to stakeholders.

• 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 analysis, visualizations, and AI-enabled insights to support corporate priorities, strategic initiatives, and informed decision-making.

The province and the Contractor shall determine changes to Services and Materials as required. The province and the Contractor will determine changes to Services and Materials through the Artifacts.