BI & AI Analyst

Top Aces•Dorval, QC

11d•Hybrid

About The Position

The BI / AI Analyst helps the business turn trusted data into better decisions. This role works directly with business teams to build Power BI dashboards, define meaningful KPIs, uncover insights, and identify practical BI/AI opportunities that improve performance across Finance, Inventory, Operations, and other priority areas. The analyst uses governed Gold-layer datasets prepared by the technical BI/Data team and focuses on reporting, insight generation, adoption, and business value. This is a business-facing role, not a heavy data engineering or advanced machine learning development role.

Requirements

Bachelor’s degree in Business Analytics, Data Analytics, Computer Science, Statistics, Engineering, Finance, Operations Management, or a related field.
3–7 years of experience in business intelligence, reporting, analytics, business analysis, or data-driven business support.
Strong hands-on Power BI experience, including dashboard design, data visualization, usability, DAX basics, and report development.
Working knowledge of SQL and structured datasets, with the ability to explore, validate, interpret, and explain business data.
Experience gathering business requirements and converting them into KPIs, dashboards, analytical outputs, and recommendations.
Ability to define and document business metrics, reporting assumptions, data definitions, and performance management outputs.
Strong communication skills, with the ability to explain insights clearly to business users, technical teams, and leadership audiences.
Collaborative mindset and ability to work effectively with SMEs, Data Product Owners, BI/Data Developers, IT, and non-technical stakeholders.

Nice To Haves

Experience in aviation, MRO, engineering, operations, finance, inventory management, supply chain, or another regulated operational environment.
Familiarity with Microsoft Fabric, Lakehouse/Warehouse concepts, semantic models, Gold-layer datasets, or governed reporting environments.
Knowledge of ERP, MRO, or enterprise systems such as Ramco, SAP, IFS, AMOS, Oracle, UKG, or HCL Notes.
Exposure to AI opportunity identification, forecasting, optimization, automation, anomaly detection, or AI-assisted analytics.
Experience with Microsoft Copilot, Power BI Copilot, or other AI productivity tools.
Experience supporting self-service analytics, data governance, reporting standards, data literacy, or user adoption initiatives.
Experience designing executive dashboards, scorecards, management reports, adoption metrics, or business value tracking reports.

Responsibilities

Build trusted business reporting: Develop clear, user-friendly Power BI dashboards and reports using governed Gold-layer datasets.
Turn business questions into insights: Translate reporting needs into dashboards, KPIs, visualizations, trends, and practical recommendations.
Support decision-making: Analyze data across Finance, Inventory, Operations, and other business areas to identify risks, opportunities, gaps, and improvement ideas.
Partner with business users: Work with SMEs to validate requirements, confirm data accuracy, interpret results, and ensure reports answer real business needs.
Strengthen KPI governance: Document metric definitions, assumptions, calculation logic, data sources, business rules, and usage guidance.
Support prioritization: Provide input to the Data Product Owner on reporting needs, dataset requests, AI use cases, business impact, adoption risks, and dependencies.
Drive adoption: Provide training, coaching, documentation, and follow-up support so users can confidently use dashboards and self-service analytics.
Measure value: Track dashboard usage, user feedback, data quality concerns, adoption levels, and business value where measurable.
Identify AI opportunities: Help business teams frame practical AI opportunities such as forecasting, optimization, anomaly detection, automation, Copilot-enabled analytics, and decision support.
Collaborate with technical teams: Work with BI/Data Developers, data owners, and IT to confirm dataset readiness, data quality issues, semantic model needs, and reporting constraints.