Our Client is seeking an Architect to provide senior-level architectural leadership and strategic direction across several initiatives. It includes leading foundational activities for Data Access such as schema analysis, data profiling, and the development of enterprise data architecture, enterprise data models, data quality (DQ) frameworks, and metadata standards. The role also guides the design and implementation of data linkages and crosswalk frameworks, enabling integrated and interoperable data across a federated data ecosystem, and supporting the development of strategic data assets.

As a Data Architect, this role contributes deep expertise in modern data platforms, architectures, and data product development practices. It supports the evolution of a Data Centre of Excellence that delivers reusable, high-quality data products and services, enabling improved data access, advanced analytics, and data-informed decision-making across the organization.

While this role is remote, work must be done from within Canada, due to network and data security issues during standard business hours (Alberta Time). Must be Canadian Citizen to apply.

• Experience dealing with clients and explaining complex data principles in a thoughtful manner. (3+ years)

• Experience in defining technical architecture, data integration, supporting software development, software configuration and deployment processes. (5+ years)

• Experience designing, planning, implementing and supporting data platforms and data solutions for large projects involving multiple data sources in an architect capacity for a large and complex environment. (5+ years)

• Experience in multiple architecture domains e.g. business, application, technology, data/information, security, and privacy. (5+ years)

• Experience modelling complex business processes and translating them into understandable IT constructs. (5+ years)

• Direct, hands-on experience performing business requirement analysis, solution architecture and design, and deployment related to data management, acquisition, preparation, quality and data lineage/provenance. (5+ years)

• Demonstrated ability to lead initiatives, facilitate workshops, and communicate effectively with senior management and cross-functional stakeholders; includes planning, estimation, presenting data and AI solutions and standards, and risk assessments. (5+ years)

• Direct, hands-on experience in business requirements analysis, architecture and design, and end-to-end delivery of cloud-based data and analytics solutions using platforms like Azure and Snowflake, covering data management, engineering, visualization, advanced analytics/data science and AI. (5+ years)

• Experience designing and delivering scalable data products (e.g., curated datasets, APIs, semantic layers) that are consumable across the organization, enabling analytics, reporting, and AI/ML use cases. (5+ years)

• Experience designing architectures that support machine learning, data science, and advanced analytics, including feature engineering, model training pipelines, MLOps, model monitoring, and use of synthetic data where applicable. (5+ years)

• Experience developing and delivering training materials and enabling teams on data, analytics, and AI best practices, governance, and platform adoption. (5+ years)

• Experience defining and implementing enterprise data and AI strategy, governance frameworks, standards, and operating models to manage data and models across their full lifecycle; includes developing technology roadmaps aligned to business objectives. (5+ years)

• Experience with Big Data and cloud-based platforms (e.g., Cloudera Data Platform) and modern architectural paradigms such as Data Fabric and Data Mesh; familiarity with AI/ML platforms and tools in cloud ecosystems. (5+ years)

• Experience with enterprise architecture frameworks (e.g. The Open Group Architecture Framework (TOGAF), Gartner or Zachman). (4+ years)

• Experience working in or for the public sector. (4+ years)

• Hands-on experience implementing data security and privacy controls, including policies, de-identification/anonymization, and data audits. Familiarity with Responsible AI principles (e.g., fairness, bias detection, explainability) and conducting risk assessments with mitigation recommendations for senior leadership. (5+ years)