Data Architect

Role Responsibilities

Join the team shaping the future of how the Government of Alberta uses data to drive decisions, deliver services, and power innovation.

The Data Management Platform is the organisation's trusted engine for Environmental and Transporation data. It exists to transform data from multiple enterprise source systems into reliable, well-governed, business-ready information products that enable decision-making across all levels of the organization

As a Data Architect within the Ministry of Technology and Innovation, you will serve in the architectural capacity for assigned data domains owning the standards, the quality checkpoints, and the design decisions that determine what reaches production and how. Working across delivery teams, platform services, business stakeholders, enterprise system owners, and BI and analytics consumers, you will ensure that data products are built to a consistent, documented standard that makes them trusted for business decision-making and ready for advanced analytics and AI consumption

This is not a design-only role. This role will hold the line at the quality gates, develop the technical capability of the people in the team, data engineering, govern a pattern library that makes every subsequent delivery faster and safer, and manage the data contracts that allow enterprise systems and AI workloads to depend on the platform reliably

This role is at the forefront of the enterprise Data Management Platform, a cloud-based, scalable environment that underpins the GoA’s digital, data, and AI strategies. This is a high-impact role where your technical leadership will help transform how data is collected, connected, secured, and used across the province.

Responsibilities:

Key responsibilities of this position include but are not limited to;

• Own and govern data architecture across assigned domains: Maintain an active pattern library and Architecture Decision Records. Hold sign-off authority at quality checkpoints, Architecture Sign-off and Data Contract Confirmed and return work that does not meet the standard before build begins
• Lead production pipeline design across multiple enterprise integration patterns: Design and govern event-driven, batch, CDC, and API-based ingestion pipelines on Azure and Databricks including Delta Lake configuration, Unity Catalog governance, orchestration, and performance tuning at production scale. Every pattern is documented, reviewed, and reusable
• Author and enforce data contracts for all certified data products: Define grain, schema, Data Quality rules, SLAs, lineage, and ownership for every dataset entering production. Manage contracts as living commitments versioning changes and designing for backward compatibility across enterprise systems, BI reports, and AI workloads consuming the same business ready layer dataset.
• Architect and govern complex downstream data sharing across multiple consumer surfaces: Design integration architecture between the platform and its consumers, semantic models, downstream enterprise operational systems, cross-domain pipelines, and AI/ML workloads. Manage breaking-change decisions and establish data sharing protocols that protect downstream consumers from upstream fragility.

Role Responsibilities contnued.

• Design data architectures that are AI-ready by default: Ensure business ready data products meet the quality thresholds, access controls, lineage, and structural completeness required for machine learning pipelines, embedded from the outset, not retrofitted. Include feature pipeline and Unity Catalog AI governance requirements in every significant design
• Embed data governance, quality, classification, and security into the delivery lifecycle: Apply privacy-by-design, Data Quality rule frameworks, and classification checkpoints from intake through to production, including sensitive data handling and governance requirements specific to a multi-ministry or multi-domain operating environment.
• Build the technical capability of analysts and engineers within assigned domains: Hold named, ongoing accountability for developing Technical Leads, Data Platform Analysts, and Integration Analysts through structured mentoring and technical guidance that produces measurable improvement in independent decision-making, data contract quality, and delivery confidence
• Represent assigned domains at cross-team architecture forums and contribute to shared platform governance: Contribute to the shared pattern library and federated architecture review process. Partner with platform services, cybersecurity, enterprise architecture, and business ministry stakeholders to ensure your domain's architectural choices are coherent with the broader data management environment

To be successful in this position, you will demonstrate:

• Architectural authority exercised with clarity. The ability to make a grounded decision, document the reasoning in a form others can use and build on, and hold the standard when delivery pressure creates shortcuts
• Structured problem-solving that finds the root cause. Particularly in data quality failures, integration design conflicts, and downstream consumer breakages where the presenting symptom and the actual problem are rarely the same thing.
• Communication that works at both ends of the room. Translating complex data architecture, systems integration, and governance concepts for delivery engineers and for senior business and government decision-makers, without losing precision in either direction

Future-Focused Skills Matter

AI is changing what data architecture means and this role is at the front of that change.

An AI workload does not simply consume a Gold layer dataset. It depends on documented lineage to be explainable, consistent quality thresholds to be reliable, versioned access controls to be auditable, and a feature structure designed for model consumption rather than reporting convenience. Architects who understand this difference, who can design business ready datasets that serves BI and a machine learning pipeline without compromising either will determine how quickly this organisation moves from data maturity to genuine AI capability.

Experience or demonstrated curiosity in any of the following is a strong asset in this role

• Unity Catalog AI governance and lineage for ML workloads    
• Agentic AI and automated data pipeline design
• Large language model integration patterns and RAG architecture
• Machine learning workflows and feature pipeline design    

If you’re excited about building the foundation for enterprise-wide AI capabilities, we encourage you to apply.

Please click on this link to view the job description for this position.

APS Competencies

Competencies are behaviors that are essential to reach our goals in serving Albertans. We encourage you to have an in depth understanding of the competencies that are required for this opportunity and to be prepared to demonstrate them during the recruitment process.

This link will assist you with understanding competencies:

https://www.alberta.ca/system/files/custom_downloaded_images/psc-alberta-public-service-competency-model.pdf.

• Agility: Ability to anticipate, assess, and readily adapt to changing priorities, manage resilience in times of uncertainty and effectively work in a changing environment.
• Drive for Results: Knowing what outcomes are important and maximizing resources to achieve results that are aligned with the goals of the organization, while maintaining accountability to each other and external stakeholders.
• Develop self and others: A commitment to lifelong learning and the desire to invest in the development of the long-term capability of yourself and others
• Systems Thinking: The work done within the APS is part of a larger integrated and inter-related environment. It is important to know that work done in one part of the APS impacts a variety of other groups/projects inside and outside the APS. Systems thinking allows us to keep broader impacts and connections in mind.
• Creative Problem Solving: Ability to assess options and implications in new ways to achieve outcomes and solutions.

Qualifications

Required: 

• University degree in a related field such as Computer Science, Information Technology, or a closely related discipline.
• Minimum of Four (4) years of directly related experience in Information Technology, including genuine domain architecture ownership, data engineering, transformation, and enterprise data architecture.

• Domain Architecture Ownership. Active, multi-domain, recent. Proven experience in enterprise data architecture, with active ownership across multiple distinct data domains within the past two years evidenced by living documentation, an approved pattern library, and production pipelines currently governed by your decisions. Experience limited to a single domain or supporting architecture under someone else's authority does not meet this criterion.
• Gate and Checkpoint Authority. Sustained, documented, multi-cycle. Proven record as a named gate or checkpoint authority across multiple delivery cycles, with documented approvals, returns of non-conforming work, and decisions that determined what reached production. Occasional peer review or informal design input does not meet this criterion
• Hands-on Azure and Databricks Platform Experience. Production-grade, personal ownership. Direct hands-on experience delivering production data pipelines on Azure using Databricks including Unity Catalog governance, and orchestration across three or more structurally distinct integration patterns, with authored ADRs currently governing those live pipelines. Certification knowledge or observer-level familiarity does not meet this criterion
• Complex Enterprise Systems Integration. Multi-system, downstream and upstream. Hands-on experience in a complex enterprise environment where the data platform sits between multiple source and consuming systems simultaneously, including at minimum one enterprise source system (e.g. ServiceNow), a BI layer with semantic model dependencies, and two downstream operational systems as a consumer with documented interface contracts and schema versioning decisions for breaking changes
• Data Contract Authorship. Repeatable practice, recent examples available. Repeatable practice of data contract authorship covering grain, schema, Data Quality rules, SLAs, source-to-target mapping, lineage, and ownership with recent examples available at interview. Includes a consistent track record of translating business logic requirements from non-technical SMEs into platform-implementable contracts across multiple domains.

Qualifications continued

• Azure Data Services Ecosystem. Breadth of production experience. Production experience across the Azure data services ecosystem beyond Databricks including ADLS Gen2, Azure AD in a data platform context, and API Management or equivalent for governed data exposure. Design decisions based on direct production experience, not certification knowledge
• Architectural Standards Enforcement. Platform integrity over delivery convenience. Active ownership of architectural standards within a delivery environment evidenced through ADRs, gate records, or documented decisions where platform integrity was upheld against delivery pressure. ADRs must cover platform-specific engineering decisions, configurations, storage optimisation, ingestion pattern selection, not documentation exercises
• Structured SDLC Practice. End-to-end, governed, multi-release. Active experience operating across the full software delivery lifecycle, from requirements intake through design, build, test, and controlled production release across multiple delivery cycles. Includes version control discipline, structured testing at each layer, release management, and change control processes. Experience limited to design or build phases without accountability across the full lifecycle does not meet this criterion.

Equivalencies: Equivalencies are considered:  

• A related two-year diploma in related discipline from a recognized post-secondary institution and six (6) years related experience; or
• A related one-year certificate from a recognized post-secondary institution and seven (7) years of related experience.

Assets: 

• Experience in a complex multi-ministry or cross-organisation data sharing governance system. 
• Experience with enterprise security, privacy-by-design, and regulated environment data architecture.
• Experience in performance engineering and cost optimisation at scale on cloud platforms.
• Experience with legacy platform modernisation or on-premises to cloud migration (including reversed decisions). 
• Named accountability for analyst or engineer professional development with verifiable outcomes
• Relevant certifications (E.g: Databricks Certified, or equivalent credentials)
• Experience with AI-ready data architecture, feature stores, MLOps integration, AI lineage governance.
• Experience in enterprise data governance, metadata management, and data catalogue implementation.

Minimum recruitment standards outline the minimum education and experience required for appointment to a job classification. 
Refer to https://www.alberta.ca/alberta-public-service-minimum-recruitment-standards

Notes

Term of Employment: Permanent full-time position.

Hours of Work: 36.25hrs/ week – Monday to Friday

Location: Greater Edmonton or Calgary area (Remote)

This position is eligible for remote work from within Greater Edmonton or Calgary area. You must reside in these areas to work remotely for this position.

Final candidates will be required to undergo security screening.

This competition may be used to fill future vacancies, across the Government of Alberta, at the same or lower classification level. 

Applicants are advised to provide a cover letter summarizing information that clearly and concisely demonstrates how their qualifications meet the advertised requirements, including education, experience, and relevant examples of required competencies. 
 

Additional Information:
In your resume, please include dates (including months and years) associated with all education and work experience. As well, please indicate whether your work experience is casual, part-time, or full-time. For example, January 15, 2006 - June 25, 2009: Assistant (PT three 8 hrs. shifts/week).

Any costs associated with obtaining the required documents/checks as noted or interview travel expenses, will be the responsibility of the candidate. Out-of-province applicants can obtain the required documents/checks from the province they currently reside in.

Links and information on what the GoA have to offer to prospective employees.

• Working for the Alberta Public Service - https://www.alberta.ca/advantages-working-for-alberta-public-service.aspx.
• Public Service Pension Plan (PSPP) - https://www.pspp.ca.
• Alberta Public Service Benefit Information - https://www.alberta.ca/alberta-public-service-benefits.
• Professional learning and development - https://www.alberta.ca/professional-development-support-directive.
• Research Alberta Public Service Careers tool – https://researchapscareers.alberta.ca.
• Positive workplace culture and work-life balance.
• Leadership and mentorship programs