Technology Product Owner, Commercial Analytics & Data - Contract - Local ORANGE COUNTY, CA REQUIRED

We are seeking a Technology Product Owner (TPO) to lead the Commercial Analytics & Data Technology Product Portfolio within the Commercial Technology organization.

The Technology Product Owner is responsible for the strategy, roadmap, delivery, adoption, and continuous improvement of analytics products that enable Commercial Operations, Sales Leadership, Sales Enablement, Finance, Marketing, and the Field Sales Organization across the company.

This role serves as the primary technology partner connecting business questions and decisions to data, analytics, engineering, and user experiences. The successful candidate will help establish end-to-end product ownership from business priority and use case through data sourcing, integration, modeling, visualization, insights, adoption, and measurable business outcomes.

The portfolio includes capabilities spanning Power BI, Qlik, commercial dashboards, forecasting and planning analytics, physician and customer intelligence, third-party data, automated reporting and distribution, AI-assisted analytics, and the underlying data and integration ecosystem. The role requires strong product leadership combined with working knowledge of analytics architecture, data models, source-to-consumption data flows, integrations, data quality, governance, and enterprise data platforms.

Key Responsibilities

Product Strategy & Portfolio Management

• Own the product vision, roadmap, and value realization strategy for the Commercial Analytics portfolio.

• Translate commercial priorities, business questions, user needs, and field feedback into an actionable analytics roadmap and prioritized product backlog.

• Partner with Commercial Operations, Sales Leadership, Finance, Marketing, Sales Enablement, and other stakeholders to define analytics use cases that improve commercial decision-making.

• Establish clear product outcomes and KPIs across areas such as analytics adoption, decision effectiveness, forecast visibility, field effectiveness, data quality, and user experience.

• Prioritize investments across run, enhancement, modernization, and innovation work based on business value, technical feasibility, dependencies, and organizational readiness.

Commercial Analytics Product Delivery

• Lead end-to-end delivery of analytics capabilities from discovery and requirements through data enablement, visualization, release, adoption, and continuous improvement.

• Manage the product backlog, feature prioritization, user stories, acceptance criteria, sprint planning, release planning, and product governance.

• Partner with Power BI and analytics developers, data engineers, architects, platform teams, analysts, data scientists, and delivery partners to execute the roadmap.

• Ensure analytics products are designed around business decisions and user workflows rather than individual reports or isolated technology requests.

• Drive adoption through stakeholder engagement, communications, training, feedback loops, usage measurement, and iterative product improvement.

BI Platforms & Analytics Experience

• Serve as product leader across commercial BI and visualization capabilities, including Power BI and Qlik.

• Guide the evolution and rationalization of the analytics experience, including enhancement of existing dashboards, Power BI adoption, and appropriate migration or decommissioning of legacy analytics where applicable.

• Promote reusable analytics patterns, common semantic definitions, consistent user experiences, and scalable dashboard design.

• Partner with analytics teams to improve performance, usability, accessibility, maintainability, and adoption of commercial analytics products.

• Evaluate opportunities for AI-assisted analytics, conversational interfaces, automated insight generation, and more effective distribution of analytics to users.

Data Models, Integration & Analytics Architecture

• Develop a strong understanding of the end-to-end commercial data landscape, including source systems, integrations, transformation logic, data models, semantic models, and consumption layers.

• Partner with data engineering and architecture teams to document and continuously improve source-to-consumption data flows and integration dependencies.

• Understand dimensional and relational data modeling concepts sufficiently to evaluate analytics requirements, identify gaps, challenge assumptions, and guide scalable solution design.

• Ensure business definitions, calculations, hierarchies, relationships, and key metrics are consistently represented across analytics products.

• Coordinate integration of internal and third-party data sources into commercial analytics products, with clear ownership of interfaces, refresh cadence, dependencies, and failure handling.

• Partner with enterprise data and platform teams to align solutions with approved data platforms, architecture patterns, integration standards, and source-of-truth principles.

Data Quality, Governance & Trust

• Establish clear ownership and governance for critical commercial data, metrics, and business definitions.

• Drive visibility into data lineage, refresh cadence, source-of-truth rules, data gaps, and quality issues that impact business decisions.

• Work with business owners, data owners, and engineering teams to define quality expectations, reconciliation processes, issue triage, and remediation priorities.

• Ensure analytics products comply with enterprise security, privacy, governance, and regulatory requirements.

• Build user trust by making data definitions, limitations, dependencies, and quality transparent and actionable.

Commercial Decision Support & Business Partnership

• Partner with business stakeholders to understand the decisions analytics must enable, including commercial performance, forecasting, field effectiveness, account and opportunity prioritization, physician/customer intelligence, and planning.

• Lead discovery, process mapping, decision mapping, journey mapping, and analytics capability assessments.

• Translate complex analytical and technical concepts into clear business language and communicate tradeoffs, risks, dependencies, and roadmap decisions effectively.

• Influence cross-functional alignment without direct authority and build trusted partnerships across business, data, analytics, and technology organizations.

AI, Advanced Analytics & Innovation

• Identify opportunities to leverage Generative AI, predictive analytics, automation, data science, and conversational analytics to improve commercial decisions and user productivity.

• Partner with data science, engineering, and platform teams to move valuable analytical concepts from experimentation into governed, scalable products.

• Ensure AI and advanced analytics use cases are grounded in trusted data, clear business outcomes, measurable value, and appropriate governance.

• Evaluate emerging analytics technologies and platform capabilities to inform the long-term Commercial Analytics roadmap.

Agile Product Operating Model

• Serve as a champion of the company Product Operating Model.

• Co-own value realization with the Business Product Owner and align a stable cross-functional squad around shared outcomes.

• Foster a culture of user-centricity, experimentation, transparency, continuous learning, and outcome-based delivery.

• Lead Agile product ceremonies and governance activities, including backlog refinement, prioritization, planning, demos, and retrospectives.

• Promote data-driven prioritization, measurable outcomes, and continuous improvement of team effectiveness.

Leadership Skills & Behaviors

• Strong product management and business transformation leadership.

• Business-first mindset with the ability to connect analytics to decisions and measurable outcomes.

• Strong analytical thinking and ability to understand complex data ecosystems without needing to be the primary developer or data engineer.

• Ability to communicate effectively across business leaders, analysts, data engineers, architects, developers, and field users.

• Strong problem-solving, facilitation, prioritization, and decision-making capabilities.

• Ability to simplify complex data and technology topics and create alignment across cross-functional teams.

• Lead through influence, collaboration, accountability, and servant leadership.

• Promote an inclusive, high-performing, and continuously learning team environment.