Sr. Data & AI Platform Integration Architect - Raleigh, NC

Gilead Sciences•Raleigh, NC

About The Position

At Gilead, we’re creating a healthier world for all people. For more than 35 years, we’ve tackled diseases such as HIV, viral hepatitis, COVID-19 and cancer – working relentlessly to develop therapies that help improve lives and to ensure access to these therapies across the globe. We continue to fight against the world’s biggest health challenges, and our mission requires collaboration, determination and a relentless drive to make a difference. Every member of Gilead’s team plays a critical role in the discovery and development of life-changing scientific innovations. Our employees are our greatest asset as we work to achieve our bold ambitions, and we’re looking for the next wave of passionate and ambitious people ready to make a direct impact. We believe every employee deserves a great leader. People Leaders are the cornerstone to the employee experience at Gilead and Kite. As a people leader now or in the future, you are the key driver in evolving our culture and creating an environment where every employee feels included, developed and empowered to fulfil their aspirations. Join Gilead and help create possible, together. Job Description At Gilead our pursuit of a healthier world for all people has yielded a cure for hepatitis C, revolutionary improvements in HIV treatment and prevention as well as advancements in therapies for viral and inflammatory diseases and certain cancers. We set and achieve bold ambitions in our fight against the world’s most devastating diseases, united in our commitment to confronting the largest public health challenges of our day and improving the lives of patients for generations to come. As a Senior Data & AI Platform Integration Architect, at Gilead you will... The Senior Data & AI Platform Integration Architect is responsible for integrating data governance controls into the enterprise Data and AI platform to ensure that enterprise data products are secure, trusted, and ready to support analytics and AI workloads. This role focuses on embedding governance policies, metadata classifications, and access controls into the data platform so that governed data can be reliably consumed by analytics systems, AI models, and intelligent agents. The role works at the intersection of governance, cloud data platforms, and emerging AI integration patterns. This role operates as a senior individual contributor and technical leader, setting platformlevel integration patterns and governance enforcement standards. While it does not have direct people management responsibility, it provides technical direction and design authority across multiple engineering and governance teams and manages vendor resources supporting platform integration and implementation. About Our Data & AI Platform Our organization operates a modern enterprise data and AI ecosystem designed to enable trusted, governed AI-ready data products that support advanced analytics and emerging AI-driven use cases. The platform is based on a data mesh architecture, where business domains publish reusable data products that can be securely discovered and consumed across the enterprise. Our enterprise data and AI platform technology stack includes: • AWS and Databricks forming the enterprise cloud data and AI platform • Informatica Intelligent Data Management Cloud (IDMC) supporting metadata management, data marketplace, governance policies, and business data quality rules • IDMC Master Data Management (MDM) and Reference Data Management (RDM) managing core enterprise business entities Enterprise data governance plays a central role in this ecosystem by ensuring that data products are trusted, well-documented, and governed for responsible use across analytics and AI initiatives. As the platform evolves, we are expanding beyond traditional SQL-based data access toward AI-ready data products enriched with business context, metadata, and semantic definitions. These capabilities enable intuitive discovery and interaction with enterprise data through natural language interfaces, AI-driven analytics tools, and emerging agentic AI systems. Role Summary To support trusted and AI-ready data products, the organization operates data governance through three complementary layers that ensure governance is defined, supported by technology, and enforced across the enterprise data and AI platform. 1. Governance Enablement This layer activates governance practices across business domains by defining governance requirements such as critical data elements, metadata standards, data quality rules, and data classification. It ensures enterprise data products are properly described, governed, and aligned with business and regulatory needs. 2. Governance Capabilities This layer provides the technology and operational capabilities that support governance across the enterprise. These capabilities enable metadata management, governance policy management, data quality monitoring, and trusted data product discovery. 3.Governance Integration This layer ensures governance policies and metadata classifications are operationalized within the enterprise data and AI platform so that governed data can be reliably used for analytics and AI applications. Together, these layers ensure enterprise data products are trusted, discoverable, and ready to support analytics and AI initiatives. The Senior Data & AI Platform Integration Architect primarily operates within the Governance Integration layer, ensuring governance policies, metadata classifications, and access controls are embedded directly within the enterprise data and AI platform. This role collaborates closely with Governance Enablement teams and Governance Capability teams to ensure governance definitions are consistently implemented across cloud data platforms and data product workflows. Why This Role Matters Now As the organization expands its use of governed data products across analytics and AI workloads, governance must extend beyond policy definition to platform-level enforcement. This role ensures governance policies, classifications, and access controls are consistently implemented across the enterprise Data & AI platform so that enterprise data products can be safely consumed across analytics platforms, AI workloads, and emerging agent-based data interaction frameworks.

Requirements

10+ years working with modern cloud data platforms and distributed data architectures.
Strong understanding of enterprise data governance principles and implementation patterns.
Experience integrating metadata, security policies, and access controls into data platforms.
Familiarity with modern data architecture patterns such as data products, data mesh, or domain-driven data architecture.
Strong working knowledge of AWS data services (Lake Formation, S3, Glue, Lambda, EventBridge, API Gateway) and Databricks (Unity Catalog, Delta Lake).
Experience implementing governance enforcement using platformnative controls
Understanding of integration patterns including APIs, service architectures, and eventdriven systems
Proficiency in Python for integration and automation engineering
Architecture design experience defining and implementing highlevel integration patterns (e.g., Hub and Spoke, PeertoPeer, Data Federation)
Strong delivery skills in Agile/Scrum environments, including backlog management and ADR documentation
Bachelor's Degree and Eight Years' Experience OR Masters' Degree and Six Years' Experience OR PhD and Two Years' Experience

Nice To Haves

Experience with AWS Lake Formation governance controls
Experience with Databricks Unity Catalog governance capabilities
Familiarity with governance platforms such as Informatica IDMC
Experience supporting analytics and AI workloads in cloud data platforms
Strong knowledge of API design patterns (REST, GraphQL, gRPC) and API management platforms (AWS API Gateway, Apigee, MuleSoft, or equivalent)
Experience with eventdriven architecture and message streaming platforms (Apache Kafka, Amazon MSK, EventBridge, SQS/SNS)
Experience designing integration solutions for AI/ML platforms or agentic architectures, including LLM API patterns and MCPbased agent tooling
Familiarity with emerging AI and agent integration patterns
Working knowledge of AIassisted software development tools (GitHub Copilot, Claude, or equivalent)
Create Inclusion - knowing the business value of diverse teams, modeling inclusion, and embedding the value of diversity in the way they manage their teams.
Develop Talent - understand the skills, experience, aspirations and potential of their employees and coach them on current performance and future potential. They ensure employees are receiving the feedback and insight needed to grow, develop and realize their purpose.
Empower Teams - connect the team to the organization by aligning goals, purpose, and organizational objectives, and holding them to account. They provide the support needed to remove barriers and connect their team to the broader ecosystem.

Responsibilities

Platform Governance Integration Integrate governance policies and metadata into AWS and Databricks environments
Implement governance enforcement using AWS Lake Formation and Databricks Unity Catalog
Implement access controls, filtering policies, and data protection rules based on governance classifications
Integrate governance metadata and classifications from governance platforms into platform enforcement layers
Automate the enforcement of data access controls, privacy classifications, and data retention policies within data platforms.
Data Product Enablement Partner with engineering teams to integrate governance requirements into data product onboarding processes
Support secure and governed consumption of enterprise data products across analytics and AI workloads
Ensure data products are onboarded with standardized governance enforcement patterns
Architecture & Integration Leadership Collaborate with Governance Enablement and Governance Capabilities teams to ensure governance policies are operationalized and enforced
Define and implement platformlevel integration patterns that support governed analytics and AI access
Expand beyond traditional SQL-based data access toward AI-ready data products enriched with business context, metadata, and semantic definitions that enable intuitive discovery and interaction with enterprise data through natural language interfaces, AI-driven analytics tools, and emerging agentic AI systems.

Benefits

This position may also be eligible for a discretionary annual bonus, discretionary stock-based long-term incentives (eligibility may vary based on role), paid time off, and a benefits package.
Benefits include company-sponsored medical, dental, vision, and life insurance plans.
For additional benefits information, visit: https://www.gilead.com/careers/compensation-benefits-and-wellbeing
Eligible employees may participate in benefit plans, subject to the terms and conditions of the applicable plans.

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