GIS & AI Analytics Engineer

The GIS & AI Analytics Engineer owns 100% of all GIS datasets, spatial strategy, and semantics, while also supporting enterprise-wide analytics across both GIS and non‑GIS domains. The role designs and delivers AI‑ready data products using Microsoft Fabric and Power BI embedding ontologies and knowledge graphs to ensure consistent meaning, relationships, and reuse across data, reports, and AI experiences.   Working AI‑natively, this role uses AI as a default accelerator for analytics, automation, and documentation, continuously identifying opportunities to eliminate manual effort and improve how users interact with data.        1) Enterprise GIS Ownership, Semantics & Strategy           Single accountable owner for GIS data, meaning, and spatial semantics.  Own 100% of all GIS datasets, including:   authoritative sources, schemas, refresh cadence, quality thresholds  spatial accuracy, lineage, versioning, and lifecycle management  Define and evolve the GIS strategy and roadmap, aligned with analytics, AI, and business outcomes.         Design and maintain a spatial ontology:   standardized definitions of assets, locations, zones, constraints, and relationships  clear differentiation between raw geometry, analytical entities, and business concepts          Contribute GIS entities into a project-wide knowledge graph connecting:   spatial assets ↔ cost ↔ schedule ↔ risks ↔ progress ↔ documents  Act as the semantic authority for all spatial concepts to ensure consistent interpretation across reports, AI agents, and users.      2) Data Platform Engineering  Builds analytics- and AI-ready data products, spatial and non-spatial.  Design and implement Bronze–Silver–Gold architecture in Microsoft Fabric:   Embed ontology-aligned IDs and relationships across datasets to support:   consistent joins between GIS and operational data  downstream knowledge graph generation              Ensure all curated datasets are:   analytics-ready (Power BI)  AI-consumable (Copilot, semantic search, reasoning)  Implement automated validation, reconciliation, and observability across layers.      3) Analytics, Visualization & Semantic Modeling (Power BI)  Transforms data into trusted insights with semantic clarity.  Build and manage Power BI semantic models grounded in:   dimensional best practices  ontology-backed definitions of measures, entities, and hierarchies           Deliver dashboards and analytical products for:   GIS datasets  non-GIS enterprise datasets  integrated spatial + operational insights          Enable self-service analytics using:   certified datasets  semantic layer reuse  consistent KPI definitions across reports  Ensure reports, metrics, and AI summaries all reference the same semantic truth.     4) AI‑Native Automation, Knowledge Graphs & Continuous Improvement  AI-first mindset; every workflow is a candidate for automation.  Work natively with AI in all aspects of delivery:   data modeling, pipeline creation, DAX/SQL/Python generation  documentation, summarization, anomaly detection           Build and evolve knowledge graphs that connect:   GIS entities  datasets, reports, KPIs  documents, decisions, and risks         Use ontologies + knowledge graphs to enable:   semantic search (“show risks affecting this zone”)  AI-generated explanations and summaries  intelligent navigation across data and reports  Observe users, tasks, and recurring requests to:   identify inefficiencies  replace manual processes with AI-assisted or automated solutions  Ensure AI usage is secure, governed, and aligned with enterprise policies. Bachelor’s degree or diploma in GIS, Geomatics, Geography, Data Analytics, Computer Science, Engineering, or a related field   Equivalent practical experience with a strong analytics/GIS portfolio considered in lieu of formal education  Min of 3 years’ experience working with: Esri or GIS-related certification (ArcGIS Pro / ArcGIS Online), Power BI Data Analyst Associate (PL‑300) or equivalent analytics certification, Microsoft Fabric, Azure Fundamentals, or data engineering certifications are an asset, GIS datasets and spatial workflows , Data analytics or data engineering in BI platforms, Hands-on experience with Power BI and analytics data modeling , Experience integrating data from multiple sources (GIS, enterprise systems, files, APIs)   Exposure to AI-assisted analytics and automation workflows  Working knowledge of Microsoft Fabric concepts (Lakehouse, pipelines, dataflows, notebooks)   Understanding of Bronze–Silver–Gold data architecture principles   Familiarity with ontologies, semantic models, or knowledge graphs (conceptual or applied)   Strong ownership mindset; able to own datasets end-to-end and act as the single source of truth   Ability to translate business questions into scalable data and analytics solutions   Analytical thinking with attention to data quality, consistency, and meaning   Clear communicator, able to work with technical and non-technical stakeholders   Organized, adaptable, and able to operate independently in a fast-moving environment  Comfort using AI as a daily productivity tool (analysis, automation, documentation, prototyping)   Ability to identify inefficiencies and proactively propose automation and AI-driven improvements  GIS & Spatial Technologies  ArcGIS Pro / ArcGIS Online / ArcGIS Enterprise or QGIS  Spatial data formats (feature services, geodatabases, shapefiles, raster)  Coordinate systems, projections, topology, and spatial QA/QC  Spatial analysis and GIS data lifecycle management  Data & Analytics Platforms  Microsoft Fabric: Lakehouse, Warehouse, Dataflows, Pipelines, Notebooks  Power BI: semantic models, DAX fundamentals, report development, performance optimization  Bronze–Silver–Gold data architecture and analytics-ready data modeling  SQL for data transformation and analysis  Automation & Integration  Data ingestion from APIs, SharePoint, files, enterprise systems  Power Automate or Fabric pipelines for automation and orchestration  Python for data processing and analytics (pandas; geopandas an asset)  AI, Semantics & Knowledge Representation  AI-assisted analytics workflows (Copilot, prompt-driven analysis, automation support)  Semantic modeling concepts and KPI standardization  Knowledge graphs and ontologies (entity definitions, relationships, reusable semantics)  Semantic search and AI-consumable data structures  Data Governance & Quality  Data validation, reconciliation, and monitoring practices  Dataset certification, access control, and documentation  Understanding of secure and governed AI usage in enterprise environments We Offer:  Competitive Salary Comprehensive Benefits Package: Disability Insurance Dental Insurance Extended medical insurance (Optional) RRSP matching Discretionary Bonus Why OTG? Welcome to Ontario Transit Group (OTG), located in the heart of Downtown Toronto, where diversity and passion collide. As we work on the groundbreaking Ontario Line project, we prioritize fostering a positive culture. Join us and be part of a team that celebrates our employees, organizes family events, and promotes health and wellness initiatives. Our commitment to personal and professional growth means annual performance reviews, salary increases, comprehensive health benefits, generous RRSP matching, industry education support, and career development opportunities.   At OTG, we embrace diversity, recognizing that it strengthens us as a team and as a company. We are an equal-opportunity employer, encouraging applications from all interested candidates. We value Indigenous people, racialized people, neurodivergent people, people with disabilities, and individuals from gender and sexually diverse communities with intersectional identities. Reasonable accommodations are available upon request for people with disabilities. If you're ready to be part of our dynamic team in one of the world's most diverse cities, don't wait any longer—apply now!   While we appreciate your interest, only selected candidates will be contacted for interviews. Please note that we do not accept agency submissions.