Manager, Enterprise Data and Analytics

Title: Manager, Enterprise Data and Analytics

Employment Type: Permanent, Full Time

Location: Ottawa, Canada – Hybrid (2 days/week in office)

Submission Date: July 29, 2026

Salary: CAD $106,000 - $118,000

Founded in 1992, Nutrition International (NI) is a global organization dedicated to delivering proven nutrition interventions to those who need them most. At Nutrition International, we make a difference, because nutrition is the difference.

Woven into the very fabric of our approach is the passion and drive of our global team of over 600 people, working in 13 offices across 11 countries with one common goal: transforming the lives of people who need it most through improved nutrition.

If you are a motivated and passionate individual who shares our conviction that a better world is possible through improved nutrition, wants to leave the world a little better than they found it, and is looking to be part of a global team with a clear vision, we want to hear from you. Please consider applying for the position below.

Reporting to the Principal Enterprise Architect, the Manager, Enterprise Data & Analytics, is responsible for the design, implementation, and operational management of Nutrition International’s enterprise data and reporting solutions.

The role focuses on transforming architectural direction into reliable data pipelines, models, and analytics-ready datasets that enable trusted reporting, dashboards, and future AI initiatives. The Manager ensures that enterprise data assets are accurate, auditable, and fit for decision‑making across Corporate Services, HR, Business Development, Programs, and across NI globally.

In this role you will :

• Design, implement, and maintain enterprise data models aligned with architectural standards and governance rules. Develop and manage data models, views, and transformations supporting the Unit’s needs. Maintain databases and NI Data warehouse.
• Design data architectures that support AI‑ready data access, including structured knowledge bases, contextual retrieval layers, and vector‑enabled data stores for Retrieval‑Augmented Generation (RAG) and agentic AI workflows.
• Collaborate with the Systems Administrator on environment configuration, security, and access requirements for data platforms (databases, Data warehouse, tables…).
• Build and maintain data pipelines that feed AI/ML models and intelligent assistants, including contextual data preparation for RAG systems, embedding generation, and integration with AI tooling via protocols such as Model Context Protocol (MCP).
• Deliver clean, trusted datasets optimized for Power BI and NI future enterprise reporting tools. Translate business reporting needs into scalable and reusable data structures. Enrich NI’s reporting maturity by implementing KPIs, aggregations, and metrics in data layers wherever possible.
• Enable AI‑augmented analytics by preparing datasets for natural language querying, contextual AI assistants, and automated insight generation, ensuring data is structured, semantically tagged, and accessible to AI agents.
• Work closely with the Principal Enterprise Architect to operationalize enterprise architecture and data standards.
• Collaborate with IM, Risk and Assurance, and other governance stakeholders to support auditability, compliance, and traceability.

For more detailed information about the role, please see the attached Job Description.

You will have a Bachelor’s or Master’s degree in Data Engineering, Computer Science, Information Systems, or related field with at least 5 to 8 years of experience in data architecture, analytics engineering, or enterprise reporting.

You will come with strong expertise in SQL, data modeling, master data management (MDM) and data warehouse/lakehouse concepts. Hands‑on experience with Power BI, Fabric, Azure data services, NSAW, Snowflake, and modern ETL / data pipeline technologies or equivalent platforms.

Demonstrated experience with AI‑enabling data practices, including Retrieval‑Augmented Generation (RAG), vector databases, embedding pipelines, context engineering, and AI integration protocols such as Model Context Protocol (MCP).

Practical knowledge…