Canadian Bank Note Company (CBN) is hiring a Data Analytics Engineer - Corporate Data Analytics Group

Internal Job Title: Data Analytics Engineer Job Type: Permanent, Full-time Location: Ottawa, Ontario Work Model: Remote Job Status: Existing Vacancy Position Summary The Data Analytics Engineer plays a critical role in preparing enterprise data to be AI-ready by designing rich semantic layers and business context that enable advanced analytics, self-service BI, and AI-powered decision-making. This role focuses on transforming curated data into trusted, governed, and reusable data products that can be safely consumed by business users, copilots, and machine learning models across the organization. Key Responsibilities Business Partnership & Domain Alignment - Partner with business stakeholders to translate domain knowledge into structured analytical and semantic representations. Semantic Modeling & Data Foundation - Design, develop, and maintain enterprise semantic models that represent business meaning, metrics, and relationships across data domains. - Collaborate with data engineers to shape silver and gold datasets that support semantic clarity and downstream AI consumption. - Build AI-ready data products by enriching datasets with business definitions, hierarchies, metadata, and contextual logic. BI Development & Metric Standardization - Develop and optimize Power BI semantic models, datasets, and metric layers to support BI, Copilot, and AI use cases. - Create and manage standardized KPIs and calculations using DAX, ensuring consistency and reuse across analytics and AI workloads. - Document business logic, data definitions, and metric context to support discoverability and AI-assisted querying. Governance, Security & Trusted Data - Implement data governance controls including row-level security (RLS), object-level security, sensitivity labels, and certified datasets. Data Product Enablement & AI Integration - Publish trusted semantic models and datasets to enable self-service and AI-assisted analytics at scale. - Support integration of analytics models with AI and ML workflows, including retrieval-augmented generation (RAG) scenarios. Performance Optimization & Reliability - Monitor and optimize model performance, refresh reliability, and query efficiency. Standards & Continuous Improvement - Contribute to analytics and AI standards and best practices through the Data & AI Community of Practice.