Applied AI Health Data Architect [Health Services- Payer]-Senior Manager

PwC•Tampa, FL

5d•$124,000 - $280,000

About The Position

As an Applied AI Health Data Architect, you will shape data architecture solutions for health services payer clients, bringing together data engineering, analytics, and AI to improve how information is structured, integrated, and used across cloud environments. As a Senior Manager in PwC’s Advisory practice and Data, Analytics & AI services, you will lead large projects, provide strategic direction for complex data modernization efforts, and guide teams and stakeholders through changing priorities and technical complexity. You will design data platforms, support cloud-native architecture decisions, and contribute to delivery across AWS, Azure, and Google Cloud Platform environments for health sector clients.

Requirements

At least a bachelor's degree.
At least 7 years of experience.

Nice To Haves

Preference for a field of study such as Analytics/Data Science, Business Administration/Management, Computer Science/Information Systems, Economics, Engineering, Finance, Mathematics/Statistics, or Operations/Supply Chain.
Experience demonstrating data architecture leadership for healthcare payer modernization initiatives.
Experience utilizing AWS, Azure, Databricks, or Snowflake for cloud data platforms.
Ability to apply AI fluency to data engineering and architecture decisions.
Ability to translate client needs into scalable data modernization roadmaps.
Experience leading complex, multi-team delivery with composure and sound judgment.

Responsibilities

Lead applied AI and health data architecture initiatives for payer data environments, translating business goals into scalable data solutions.
Design cloud-native data platforms, pipelines, and integration patterns across AWS, Azure, Databricks, and Snowflake.
Develop data models, warehouse structures, and transformation logic to support analytics, AI workloads, and operational reporting.
Guide data modernization efforts to migrate legacy systems into modern architectures with improved performance, security, and usability.
Oversee data quality, validation, governance, and anonymization practices across sensitive health data domains.
Apply data engineering methods to tune pipelines, optimize queries, and improve database and warehouse performance.
Advise client stakeholders on data strategy, architecture decisions, and AI-ready platform design within complex payer environments.
Lead teams through delivery planning, issue resolution, and technical coordination across multiple workstreams.
Coach practitioners on data engineering platforms, digital tooling, and AI-enabled solution delivery.
Present solution options, tradeoffs, and implementation paths that support modernization priorities and future data capabilities.