Data Analytics Engineer - Corporate Data Analytics Group

Canadian Bank Note Company

8h•Remote

About The Position

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.

Requirements

Legally eligible to work in Canada.
Fluent in English (speak, read, write).
Able to obtain (in a timely manner) and maintain Government of Canada Secret (Level II) security clearance.
Bachelor’s degree in Computer Science, Data Science, Information Systems, Engineering, or a related field.
Understanding of semantic modeling concepts including metrics, hierarchies, and dimensional modeling.
Understanding of data governance principles and secure data access patterns.
8+ years of relevant professional experience, including:
3+ years of professional experience in analytics, business intelligence, or data modeling roles.
3+ years of hands-on experience with Power BI semantic modeling, DAX, and dataset design.
3+ years of experience working with SQL-based analytical datasets.
Ability to translate business concepts into structured analytical and semantic representations.

Nice To Haves

Microsoft Certifications: Power BI Data Analyst Associate (PL-300) or Fabric Analytics Engineer Associate (DP-600).
Knowledge of data catalogs, business glossaries, and metadata management.
Experience with Microsoft Fabric semantic models, OneLake, and data products.
Experience preparing datasets for AI/ML or Copilot scenarios (e.g., feature tables, RAG inputs).
Experience in manufacturing, software, or regulated environments.

Responsibilities

Partner with business stakeholders to translate domain knowledge into structured analytical and semantic representations.
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.
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.
Implement data governance controls including row-level security (RLS), object-level security, sensitivity labels, and certified datasets.
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.
Monitor and optimize model performance, refresh reliability, and query efficiency.
Contribute to analytics and AI standards and best practices through the Data & AI Community of Practice.