BI and Data Analytics Product Manager

e-EMPHASYS TECHNOLOGIES INC•Cary, NC

About The Position

The Data & Analytics Product Manager owns the end‑to‑end data product strategy for a modern heavy‑equipment dealership platform. This role is responsible for transforming raw operational data from core dealer systems into trusted, governed, AI‑compatible data products that power dashboards, analytics, and intelligent agents. The role sits at the intersection of dealer business operations (Sales, Service, Parts, Rental), Microsoft Fabric‑based data architecture, and Power BI for semantic modeling, ensuring data is not only reported—but actionable, predictive, and consumable by AI.

Requirements

Hands‑on experience with: Microsoft Fabric Lakehouse architectures Power BI semantic models Ability to translate operational workflows into data models.
Strong grasp of: Dimensional modeling KPI governance Data quality and metric consistency
Experience designing data models for AI and natural language interaction
Proven product management experience owning data platforms or analytics products.
Ability to define roadmaps, prioritize backlogs, and measure business impact.
Comfortable operating between technical and business stakeholders.

Nice To Haves

Deep understanding of heavy equipment dealership operations is a plus.
Machine sales & rental
Service operations and technician productivity
Parts inventory and fill rates
Asset utilization and lifecycle profitability

Responsibilities

Data Product Strategy & Vision
Define and own the data product roadmap aligned to dealership outcomes such as margin optimization, asset utilization, service productivity, inventory health, fleet optimization
Establish domain‑based data products (Sales, Service, Parts, Rental, Finance) with clear ownership and success metrics.
Translate dealer pain points into analytics, KPIs, and AI‑ready data capabilities, not just reports.
Microsoft Fabric & Lakehouse Ownership
Own the Data Lakehouse strategy
Define standards for: Data ingestion from core applications (ERP/DMS, CRM, Rental, Telematics). Entity mapping (Customer, Equipment, Work Orders, Parts, Assets). Data quality, lineage, and governance.
Partner with Data Engineering to prioritize pipelines that unlock business value, not just data availability.
Semantic Layer & Power BI
Own the enterprise semantic model used across Power BI, self‑service analytics, and AI agents.
Ensure: Consistent KPI definitions across departments. Role‑based metrics for executives, managers, and frontline leaders. Reusable, well‑documented datasets that reduce report sprawl.
Drive adoption of action‑oriented dashboards used in daily operations, not just monthly reviews.
AI‑Ready Data & Copilot Enablement
Design semantic models that are compatible with AI and Copilot experiences, enabling: Natural language querying. Automated insight generation. Anomaly detection and trend identification.
Partner with AI and product teams to enable data‑driven agents that surface insights instead of static reports.
Stakeholder & Cross‑Functional Leadership
Act as the single point of accountability for analytics outcomes across Product, Engineering, GTM, and Customer Success.
Work closely with: Engineering (data pipelines, Fabric optimization). BI Analysts (dashboard design, KPI validation). Business leaders (Sales, Service, Parts, Rental) ensure relevance and adoption.
Balance short‑term reporting needs with long‑term data platform scalability.