{"id":1033930,"date":"2026-06-27T04:01:16","date_gmt":"2026-06-27T08:01:16","guid":{"rendered":"https:\/\/therisk.global\/nexus-agency\/?post_type=job_listing&p=1033930"},"modified":"2026-06-27T04:01:16","modified_gmt":"2026-06-27T08:01:16","slug":"chief-data-architect-senior-data-engineering-lead","status":"publish","type":"job_listing","link":"https:\/\/therisk.global\/nexus-agency\/job\/chief-data-architect-senior-data-engineering-lead\/","title":{"rendered":"Chief Data Architect \/ Senior Data Engineering Lead"},"content":{"rendered":"

Location:<\/strong> Canada, remote or hybrid
\nFunction:<\/strong> Data Architecture, Data Engineering, Microsoft Fabric, Azure Data, Power BI, Geospatial Data, AI Infrastructure, Evidence Systems
\nSeniority:<\/strong> Principal \/ Staff+ \/ Executive Technical Leadership
\nEngagement:<\/strong> Full-time, fractional-to-full-time, senior contractor, technical partner or advisory-to-executive pathway<\/p>\n

About the Role<\/strong><\/h2>\n

Nexus Agency is recruiting a Chief Data Architect \/ Senior Data Engineering Lead<\/strong> for a frontier-technology platform at the intersection of AI, Microsoft Fabric, Azure data infrastructure, geospatial intelligence, climate and hazard analytics, edge computing, secure infrastructure, evidence systems and decision-support workflows<\/strong>.<\/p>\n

This is a build-stage senior technical role for someone who can design the data foundation that makes the entire platform work.<\/p>\n

We are looking for a senior data leader who can turn complex, fragmented, multi-source information into a usable, governed and scalable data architecture. The right candidate can design ingestion pipelines, data contracts, harmonization logic, metadata systems, lineage, quality controls, geospatial data models, source-readiness workflows and decision-grade data products across the Microsoft data ecosystem.<\/p>\n

This role requires strong fluency across the Azure and Microsoft data stack<\/strong>, including Microsoft Fabric, OneLake, Lakehouse, Warehouse, Data Factory, Dataflows Gen2, Real-Time Intelligence, Eventstream, KQL, Power BI, semantic models, Microsoft Purview, Azure Data Lake Storage, Azure SQL, Azure Synapse, Dataverse, Azure AI Search, Azure OpenAI, Foundry Tools in Fabric, Fabric IQ, ontology-enabled analytics and Microsoft governance patterns.<\/p>\n

This is not a reporting or BI role. It is not a narrow analytics role. It is the data architecture role behind a serious AI, geospatial and risk-intelligence platform.<\/p>\n

If you want to build frontier data infrastructure that connects AI, GIS, Earth observation, climate risk, Microsoft Fabric, Azure, Power BI, ontology-driven intelligence, edge deployment and public-interest technology, this role is for you.<\/p>\n

What You Will Own<\/strong><\/h2>\n

You will own the data architecture and data engineering strategy for a platform that brings together:<\/p>\n

    \n
  • Microsoft Fabric architecture, OneLake, Lakehouse, Warehouse and Data Factory patterns<\/li>\n
  • Azure Data Lake Storage, Azure SQL, Azure Synapse and Azure-native data services<\/li>\n
  • Power BI semantic models, dashboards, reports and operational decision views<\/li>\n
  • Fabric IQ, ontology-enabled analytics and semantic data products<\/li>\n
  • Microsoft Purview governance, cataloguing, lineage, classification and policy alignment<\/li>\n
  • Dataverse and Power Platform data integration where relevant<\/li>\n
  • Azure AI Search, Azure OpenAI and Foundry Tools in Fabric for AI-assisted data workflows<\/li>\n
  • structured, unstructured, spatial, temporal, sensor and operational data<\/li>\n
  • geospatial data, GIS layers and ArcGIS-oriented outputs<\/li>\n
  • Earth observation, remote sensing and site-context data<\/li>\n
  • climate, hazard, resilience and risk datasets<\/li>\n
  • local inputs, telemetry and mission-site information<\/li>\n
  • data ingestion, transformation and harmonization pipelines<\/li>\n
  • metadata, lineage, provenance and data-quality systems<\/li>\n
  • source-readiness and data-access workflows<\/li>\n
  • data products for AI models, dashboards, maps and decision-support tools<\/li>\n
  • evidence packages, validation records and acceptance documentation<\/li>\n
  • secure data handling, role-based access and privacy-aware design<\/li>\n
  • open-source public-good data patterns<\/li>\n
  • enterprise-grade data pipelines and customer-specific data environments<\/li>\n<\/ul>\n

    You will make the data layer reliable, explainable, traceable, secure, governed and usable.<\/p>\n

    The Core Technical Challenge<\/strong><\/h2>\n

    The platform depends on data that is messy, distributed, multi-format, multi-sector and often incomplete.<\/p>\n

    Your job is to make that data usable without pretending it is perfect.<\/p>\n

    You will design the architecture that allows technical teams to ingest, validate, normalize, enrich, geocode, join, score, document and serve data across AI, GIS, edge, dashboard, evidence and reporting workflows.<\/p>\n

    You will help decide:<\/p>\n

      \n
    • which sources are ready;<\/li>\n
    • which sources need validation;<\/li>\n
    • which data belongs in open reference layers;<\/li>\n
    • which data must remain controlled;<\/li>\n
    • how Microsoft Fabric should be structured across workspaces, lakehouses, warehouses and semantic models;<\/li>\n
    • how OneLake, shortcuts and Delta tables should be used;<\/li>\n
    • how Data Factory, Dataflows Gen2, Eventstream and Real-Time Intelligence should support ingestion and streaming;<\/li>\n
    • how Power BI semantic models should serve decision-support and operational reporting;<\/li>\n
    • how Fabric IQ, ontologies and semantic layers should represent entities, relationships, risks, sites, hazards, assets and decisions;<\/li>\n
    • how Microsoft Purview should support cataloguing, governance, classification, lineage and data discovery;<\/li>\n
    • how Dataverse, Power Platform and enterprise business data should connect where appropriate;<\/li>\n
    • how Azure AI Search, Azure OpenAI and Foundry Tools should support retrieval, enrichment, classification and AI-assisted workflows;<\/li>\n
    • how geospatial data should be structured for GIS and ArcGIS-oriented outputs;<\/li>\n
    • how AI and modelling teams should consume data;<\/li>\n
    • how data outputs should be tested, documented and accepted; and<\/li>\n
    • how the data platform should scale across sites, partners, programs and deployments.<\/li>\n<\/ul>\n

      Microsoft Data Stack Fluency Required<\/strong><\/h2>\n

      This role requires deep practical fluency across the Microsoft and Azure data ecosystem. Strong candidates should understand how to architect and operate across many of the following:<\/p>\n

        \n
      • Microsoft Fabric:<\/strong> OneLake, Lakehouse, Warehouse, Data Engineering, Data Factory, Dataflows Gen2, notebooks, pipelines, Real-Time Intelligence, Eventstream, KQL databases, Fabric Data Agents, Fabric IQ, Copilot in Fabric and Fabric governance patterns<\/li>\n
      • Power BI:<\/strong> semantic models, report architecture, dashboard design, Direct Lake, Power Query, DAX, dataflows, workspace governance, row-level security, deployment pipelines and executive \/ operational reporting<\/li>\n
      • Azure Data Platform:<\/strong> Azure Data Lake Storage Gen2, Azure SQL Database, Azure SQL Managed Instance, Azure Synapse Analytics, Azure Cosmos DB, Azure Database services, Azure Data Factory, Azure Functions and Azure Event Hubs where relevant<\/li>\n
      • Governance and Security:<\/strong> Microsoft Purview, data catalogue, lineage, classification, sensitivity labels, access policies, role-based access control, Microsoft Entra ID, private endpoints, managed identities and secure data sharing<\/li>\n
      • AI and Search:<\/strong> Azure AI Search, Azure OpenAI, Foundry Tools in Fabric, AI Functions, embeddings, vector search, retrieval-augmented generation, AI enrichment, classification and model-ready data workflows<\/li>\n
      • Dataverse and Power Platform:<\/strong> Dataverse, Power Apps, Power Automate, Dynamics 365 data integration, Synapse Link, Fabric Link and enterprise business-data integration patterns<\/li>\n
      • Ontology and Semantic Layer:<\/strong> ontology design, entity modelling, relationship graphs, semantic models, knowledge layers, risk ontologies, asset \/ site \/ hazard models and decision-support semantics<\/li>\n
      • Geospatial Data:<\/strong> spatial databases, PostGIS, ArcGIS-oriented data outputs, OGC-compatible services, STAC-compatible workflows, Earth observation, remote sensing, raster \/ vector data, coordinate systems and spatial analytics<\/li>\n
      • Engineering Practices:<\/strong> Python, SQL, PySpark, T-SQL, Spark, Delta Lake, notebooks, CI\/CD, Git, infrastructure-as-code, testing, validation, monitoring and documentation<\/li>\n<\/ul>\n

        You do not need to have used every Microsoft product in production. You do need the architecture judgment to choose the right services, avoid unnecessary complexity, and build a coherent data foundation across Microsoft, Azure, geospatial and AI systems.<\/p>\n

        What You Will Deliver<\/strong><\/h2>\n

        In the first 90 to 180 days, you should be able to lead or produce:<\/p>\n

          \n
        • Microsoft Fabric architecture and workspace model<\/li>\n
        • OneLake, Lakehouse, Warehouse and semantic model strategy<\/li>\n
        • data architecture and data-fabric model<\/li>\n
        • ingestion and harmonization architecture<\/li>\n
        • Data Factory, Dataflows Gen2 and pipeline design<\/li>\n
        • Real-Time Intelligence and Eventstream pattern where relevant<\/li>\n
        • Power BI semantic model and reporting architecture<\/li>\n
        • Fabric IQ \/ ontology-enabled data model plan<\/li>\n
        • Purview governance, catalogue, lineage and classification model<\/li>\n
        • source-readiness framework<\/li>\n
        • data contract templates<\/li>\n
        • metadata and lineage model<\/li>\n
        • data-quality and validation approach<\/li>\n
        • geospatial data model<\/li>\n
        • spatial database and GIS data architecture<\/li>\n
        • AI-ready data pipeline design<\/li>\n
        • Azure AI Search \/ Azure OpenAI retrieval and enrichment pattern<\/li>\n
        • evidence-data and auditability framework<\/li>\n
        • data access, role-control and security model<\/li>\n
        • open-source data schema and reference-data plan<\/li>\n
        • enterprise data pipeline and deployment plan<\/li>\n
        • data engineering roadmap<\/li>\n
        • data risk register<\/li>\n
        • documentation and handoff model<\/li>\n
        • staffing plan for data engineering and data operations<\/li>\n<\/ul>\n

          The goal is to create a data foundation that can move from prototype and testing into repeatable, trusted and scalable platform delivery.<\/p>\n

          What You\u2019ll Do<\/strong><\/h2>\n
            \n
          • Own the data architecture across ingestion, transformation, storage, metadata, lineage, quality, access and delivery.<\/li>\n
          • Design the data-fabric model that connects Microsoft Fabric, Azure, AI, GIS, edge systems, dashboards, evidence workflows and decision-support tools.<\/li>\n
          • Architect OneLake, Lakehouse, Warehouse, semantic models, Power BI and Fabric workspace structures.<\/li>\n
          • Build source-readiness processes for evaluating whether data is usable, incomplete, restricted, sensitive, stale, duplicative or unsuitable.<\/li>\n
          • Define data contracts for internal teams, partners, APIs, GIS layers, models, dashboards and deployment environments.<\/li>\n
          • Lead data engineering patterns for batch, streaming, spatial, sensor, climate, hazard, operational and document-based data.<\/li>\n
          • Use Data Factory, Dataflows Gen2, notebooks, pipelines, Spark, SQL, Real-Time Intelligence and Eventstream patterns where appropriate.<\/li>\n
          • Structure geospatial data pipelines for site-context layers, ArcGIS-oriented outputs, map services and spatial analytics.<\/li>\n
          • Support AI and modelling teams with clean, documented, versioned and validated datasets.<\/li>\n
          • Build metadata, lineage and provenance systems that make data outputs explainable and auditable.<\/li>\n
          • Use Microsoft Purview and related governance patterns for cataloguing, classification, lineage, access and data discovery.<\/li>\n
          • Design semantic models, ontologies and knowledge layers that make complex risk, site, hazard, asset and decision relationships usable.<\/li>\n
          • Integrate Azure AI Search, Azure OpenAI and Foundry Tools where useful for retrieval, enrichment, classification and AI-assisted workflows.<\/li>\n
          • Design secure data access patterns, role-based controls, data retention logic and privacy-aware workflows.<\/li>\n
          • Define which schemas, examples and reference datasets can be open-source and which customer-specific or sensitive data must remain controlled.<\/li>\n
          • Support testing, validation, technical acceptance, evidence packs and post-delivery data documentation.<\/li>\n
          • Help recruit and structure the data team across data engineering, data architecture, geospatial data, data quality, data governance, analytics engineering and Power BI \/ Fabric implementation.<\/li>\n<\/ul>\n

            Who You Are<\/strong><\/h2>\n

            You are a senior data architect or data engineering leader who has built complex data systems before.<\/p>\n

            You may be a Chief Data Architect, Head of Data Engineering, Principal Data Engineer, Staff Data Engineer, Data Platform Architect, Microsoft Fabric Architect, Azure Data Architect, Power BI Architect, Geospatial Data Architect, Data Infrastructure Lead or technical founder with deep data-platform experience.<\/p>\n

            You are comfortable working with incomplete information, messy sources, spatial data, institutional constraints, security requirements and real users who need reliable outputs.<\/p>\n

            You can design systems, write standards, guide engineers, challenge assumptions, define data contracts, review schemas, structure pipelines, make build-versus-buy decisions and explain data architecture to technical and non-technical stakeholders.<\/p>\n

            You understand that data architecture is not just storage and pipelines. It is the foundation for AI, GIS, decision support, evidence, governance, trust and scale.<\/p>\n

            What We\u2019re Looking For<\/strong><\/h2>\n

            Strong candidates will bring experience across several of the following areas:<\/p>\n

              \n
            • Microsoft Fabric architecture and implementation<\/li>\n
            • OneLake, Lakehouse, Warehouse, Data Factory, Dataflows Gen2 and notebooks<\/li>\n
            • Power BI semantic models, dashboards, reports, Direct Lake, DAX and Power Query<\/li>\n
            • Microsoft Purview, data cataloguing, lineage, classification and governance<\/li>\n
            • Fabric IQ, ontology, semantic layer or knowledge-model design<\/li>\n
            • Azure Data Lake Storage, Azure SQL, Azure Synapse, Azure Data Factory or Azure-native data platforms<\/li>\n
            • Azure AI Search, Azure OpenAI, Foundry Tools, embeddings, vector search or AI-assisted data workflows<\/li>\n
            • Dataverse, Power Platform, Dynamics 365 or enterprise business-data integration<\/li>\n
            • data platforms, lakehouse, warehouse or data-fabric design<\/li>\n
            • ingestion pipelines, ETL, ELT, streaming or event-driven data systems<\/li>\n
            • metadata, lineage, provenance and data-quality systems<\/li>\n
            • geospatial data, PostGIS, spatial databases, ArcGIS or enterprise GIS workflows<\/li>\n
            • Earth observation, remote sensing, climate, hazard, infrastructure or sensor data<\/li>\n
            • AI-ready datasets, feature stores, model-data pipelines or ML data operations<\/li>\n
            • APIs, data contracts, schemas and interoperability patterns<\/li>\n
            • secure data access, role-based controls and privacy-aware data handling<\/li>\n
            • cloud, hybrid cloud or controlled-environment data platforms<\/li>\n
            • open-source data schemas, public datasets or developer-facing data tools<\/li>\n
            • enterprise data delivery, regulated-sector data or public-sector data environments<\/li>\n
            • technical documentation, testing, validation and acceptance<\/li>\n<\/ul>\n

              No candidate needs to be world-class in every domain. The right candidate knows how to architect across domains, identify gaps, recruit the right people and build data systems that survive real-world constraints.<\/p>\n

              You\u2019ll Stand Out If You Have<\/strong><\/h2>\n
                \n
              • Built a Microsoft Fabric or Azure data platform from prototype to production.<\/li>\n
              • Designed data architecture for AI, GIS, climate, infrastructure, emergency management, risk or resilience systems.<\/li>\n
              • Worked with Power BI semantic models, Fabric Lakehouse, OneLake, Data Factory or Purview in production environments.<\/li>\n
              • Designed ontology, entity-relationship, semantic-layer or knowledge-graph models for complex domains.<\/li>\n
              • Worked with geospatial data, ArcGIS, PostGIS, Earth observation or remote sensing.<\/li>\n
              • Built ingestion and harmonization pipelines across messy, multi-source data.<\/li>\n
              • Created metadata, lineage, data-quality or data-governance systems.<\/li>\n
              • Supported AI or modelling teams with reliable data products.<\/li>\n
              • Worked in public-sector, regulated, critical-infrastructure, research or institution-facing environments.<\/li>\n
              • Produced data architecture documents, data contracts, schemas, source-readiness models or acceptance documentation.<\/li>\n
              • Built or led a data engineering team in a fast-moving, high-accountability environment.<\/li>\n<\/ul>\n

                Why Join<\/strong><\/h2>\n

                This is an opportunity to help build frontier data infrastructure from Canada with global relevance.<\/p>\n

                The work sits at the intersection of AI, Microsoft Fabric, Azure, geospatial intelligence, climate risk, resilience, secure infrastructure, edge computing and public-interest technology. It is for people who want to build data systems that matter, not just another dashboard pipeline.<\/p>\n

                You will help define the data architecture, shape the data engineering culture, support open and enterprise pathways, and contribute to a platform designed for real-world risk, resilience and decision-support use cases.<\/p>\n

                What Success Looks Like<\/strong><\/h2>\n

                A successful Chief Data Architect \/ Senior Data Engineering Lead will:<\/p>\n

                  \n
                • turn fragmented data requirements into a clear Microsoft Fabric, Azure and geospatial data architecture;<\/li>\n
                • establish the data-fabric model for AI, GIS, edge, dashboard, evidence and decision-support workflows;<\/li>\n
                • define OneLake, Lakehouse, Warehouse, semantic model, Power BI and Purview patterns;<\/li>\n
                • establish ontology-enabled data models for sites, assets, hazards, risks, evidence, decisions and operational workflows;<\/li>\n
                • define data contracts, ingestion patterns, metadata, lineage and validation controls;<\/li>\n
                • build clear boundaries between open reference data patterns, enterprise data pipelines and customer-specific data environments;<\/li>\n
                • guide data engineering across spatial, sensor, climate, hazard, operational and AI-ready datasets;<\/li>\n
                • recruit or coordinate the data team needed to execute;<\/li>\n
                • create data systems that can be tested, validated, documented, deployed, supported and scaled; and<\/li>\n
                • build data credibility with engineers, partners, customers and institutional stakeholders.<\/li>\n<\/ul>\n

                  What This Role Is Not<\/strong><\/h2>\n

                  This is not a dashboard-only role, reporting role or generic data-management position.<\/p>\n

                  It is not for someone who only wants to run analytics after the data is already clean. It is not for someone who treats Power BI as the whole data strategy, Fabric as only a reporting layer, or governance as paperwork.<\/p>\n

                  It is not for someone who avoids messy source problems, geospatial complexity, ontology design, technical documentation, security constraints or data-quality accountability.<\/p>\n

                  This is a builder role for someone who can create the data foundation that AI, GIS, edge systems, evidence workflows and decision-support products depend on.<\/p>\n

                  Compensation<\/strong><\/h2>\n

                  Initial 90-Day Execution Mandate<\/strong><\/p>\n

                  This role may begin with a structured 90-day execution mandate. During this period, the selected candidate will be expected to deliver defined technical outputs, including system architecture, technical roadmap, open-source and enterprise stack boundaries, data and geospatial architecture, edge deployment approach, AI\/model integration plan, technical risk register, team structure, and testing \/ acceptance framework.<\/p>\n

                  Depending on engagement structure, the initial period may be set up as a paid probationary employment period or a milestone-based consulting engagement. Successful delivery may lead to a confirmed ongoing CTO \/ Founding Systems Architect appointment. The initial period may count toward employment tenure where the candidate is engaged as an employee from day one.<\/p>\n

                  Eligibility<\/strong><\/h2>\n

                  Candidates must be legally authorized to work in Canada or in the applicable role location. Canada-based candidates are strongly encouraged to apply.<\/p>\n

                  Equal Opportunity<\/strong><\/h2>\n

                  Nexus Agency welcomes qualified candidates from diverse professional, geographic, cultural, technical, institutional and sectoral backgrounds.<\/p>\n

                  We are especially interested in senior data leaders who can bridge Microsoft Fabric, Azure data infrastructure, Power BI, AI, geospatial systems, cybersecurity, climate resilience, evidence systems and public-interest technology.<\/p>\n

                  Talent Pipeline Notice<\/strong><\/h2>\n

                  This is a continuous strategic talent posting. Candidates may be considered for current or future opportunities across Nexus Agency, Nexus Consortiums, Nexus-aligned companies, partner organizations, public-interest technology programs, projects and implementation vehicles.<\/p>\n

                  Application, participation, expression of interest or introduction does not guarantee selection, appointment, compensation, contract award, placement, public authority, procurement approval, endorsement, certification, financeability, insurability or implementation authority.<\/p>\n

                  How to Apply<\/strong><\/h2>\n

                  Please submit:<\/p>\n

                    \n
                  • resume or executive profile<\/li>\n
                  • LinkedIn profile or professional biography<\/li>\n
                  • short statement of interest<\/li>\n
                  • summary of work authorization<\/li>\n
                  • examples of Microsoft Fabric, Azure data, Power BI, ontology, data-platform, geospatial data, AI-ready data, enterprise deployment or technical team work led<\/li>\n
                  • preferred location, engagement model and availability<\/li>\n<\/ul>\n

                    Applications will be reviewed on a rolling 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