Business Data Modeler & Strategist

T-Mobile•Overland Park, NY

13d•Hybrid

About The Position

The Business Analysis Manager – Data Modeler serves as the critical link between business stakeholders and technology teams, ensuring that enterprise data structures, semantic layers, and reporting frameworks are designed to support business strategy, operational execution, and decision-making at all levels of the organization. This role partners closely with business leaders to understand processes, metrics, and decision-making needs, translating those requirements into scalable data models and semantic layer designs that enable consistent, trusted, and actionable insights. The position is deeply integrated into day-to-day business operations, maintaining a comprehensive understanding of evolving business priorities while collaborating with IT, data engineering, and architecture teams to deliver sustainable data solutions. The successful candidate combines strong business acumen with hands-on expertise in data modeling, semantic layer design, and analytics architecture. They possess the ability to design data structures that support frontline operational reporting, management performance tracking, and executive-level strategic insights while ensuring alignment with enterprise data standards and governance practices.

Requirements

Bachelor's Degree plus 5 years of related work experience OR Advanced degree with 3 years of related experience (Required)
Acceptable areas of study include Data analysis, data science, similar quantitative fields or equivalent practical experience (Required)
4-7 years Working experience solving analytical problems using quantitative approaches (Required)
4-7 years Working experience reporting and analyzing performance & data visualization (Tableau, Power BI, etc.) (Required)
4-7 years Working experience in advanced analytics, which includes SQL analysis of complex datasets, competitive analysis, quantitative analysis & research (Required)
At least 18 years of age
Legally authorized to work in the United States

Nice To Haves

Working experience with Python, R, Go or similar statistics or data science language preferred.
Telecommunications (Preferred)
(Micro)Strategy tool experience (Preferred)
Power Automation Suite Experience, including Power apps (Preferred)
Business Acumen (Required)
Dashboard Development (Required)
Data Analysis (Required)
Data Storytelling (Required)
Executive Presence (Required)
Financial Modeling (Required)
People Management (Required)
Project Management (Required)
Stakeholder Management (Required)
Strategic Thinking (Required)
Other Business Certification (Preferred)

Responsibilities

Partner with business leaders to understand strategic objectives, operational processes, KPIs, and reporting requirements.
Translate business needs into scalable data models, semantic layers, and analytical frameworks.
Act as the primary liaison between business stakeholders and IT teams to ensure business requirements are accurately represented in technical solutions.
Drive alignment on business definitions, metrics, hierarchies, and data standards across organizations.
Identify opportunities to improve data accessibility, usability, and consistency across the enterprise.
Design and maintain conceptual, logical, and business-facing data models that support enterprise analytics and reporting needs.
Lead the design and governance of semantic layers to ensure consistent business definitions and metric calculations across reporting platforms.
Define data relationships, hierarchies, dimensions, measures, and business rules to support scalable self-service analytics.
Partner with data engineering and architecture teams to ensure data structures align with business requirements and analytical use cases.
Validate that data models effectively support frontline operations, leadership reporting, and executive decision-making.
Collaborate with IT, Data Engineering, Data Governance, and Business Intelligence teams to prioritize and deliver data solutions.
Participate in agile planning, requirements gathering, user acceptance testing, and solution validation activities.
Provide guidance and oversight for new data initiatives, ensuring business outcomes remain at the center of solution design.
Drive continuous improvement in data quality, usability, and adoption.
Influence enterprise data strategy by advocating for scalable and business-centric data solutions.