Director, Data Product Architecture - Healthcare Analytics Solutions

Overview

Healthcare Analytics Solutions (HAS) is an innovative team within Quest Diagnostics that leverages Quest data to develop products and services to solve the challenges and improve outcomes in healthcare across many different markets (Pharma, Clinical Trials, Health Plans/Payers, Hospitals/Health Systems, and Public Health agencies).

The Director, Data Product Architecture leads and manages the HAS technology infrastructure and architectural framework to align with best practices, data and AI governance standards, and the broader enterprise architecture. The role will be responsible for creating, documenting and promoting the innovation plan of record for HAS technology processes within the data organization, ensuring that our strategies comply with industry standards and the parent company’s requirements. This role includes liaising with architecture, security, data sharing, and AI governance review boards to ensure alignment and compliance across all initiatives.

Responsibilities

In this role, the Director will possess a successful mix of technical expertise, organizational leadership, and the ability to collaborate across various teams across Quest Diagnostics. The Director will ensure that our architecture and processes align with Quest’s enterprise data and cloud infrastructure, while supporting the growth and maturity of HAS data science capabilities.

• Coordinates and manages the HAS data and machine learning architecture plans, ensuring alignment with organizational goals and parent company requirements.
• Codifies, documents, and disseminates data science best practices throughout HAS in collaboration with the Data Science Community of Practice leader.
• Collaborates with Quest teams responsible for cloud infrastructure, security, and enterprise data architecture to ensure our systems and processes are integrated into the larger corporate framework.
• Serves as the primary liaison with the parent company’s governance bodies, including the architecture review board, security review board, data sharing review board, and AI governance review board.
• Leads the development of internal processes for documenting data science models, pipelines, and data architecture decisions.
• Manages the documentation and review processes for key data architecture decisions, ensuring clarity and alignment across teams.
• Supports ongoing communication and training within the organization to promote understanding of data governance and architecture best practices.
• Tracks changes in industry trends, regulations, and technologies related to data governance, architecture, and security, ensuring that the organization remains compliant and up to date.

Qualifications

Required Work Experience:

• 5 years of relevant experience in data architecture, data governance, or a related field, with a focus on compliance and enterprise systems integration
• Proven experience liaising with multiple governance boards (e.g., architecture, security, AI) and ensuring project compliance.

Preferred Work Experience: 

• 7 years of experience in data architecture, data governance, or a related field, with a focus on compliance and enterprise systems integration.
• Experience in managing and aligning data architecture plans with a broader corporate enterprise.
• Experience working with healthcare data, including an understanding of regulatory requirements such as HIPAA and GDPR.

Skill Sets and Attributes: 

• The ability to quickly grasp the technical implications of business processes, and the ability to provide valuable insight into the perspectives of users, managers, developers, and other stakeholders.
• Agility to comfortably move between highly varying levels of abstraction, from business strategy, to IT strategy, to high-level technical design.
• Expertise in technology infrastructure and architectural patterns.
• Experience with AI governance principles.
• Strong understanding of cloud infrastructure (GCP, AWS, or Azure), with specific knowledge of security and governance best practices.
• Familiarity with machine learning and data science processes, including MLOps and data pipelines.
• Knowledge of medical coding systems, including LOINC, ICD10, and CPT.
• Certification in data governance or cloud architecture (e.g., TOGAF, CDMP).
• Excellent documentation and communication skills, with the ability to distill complex architectural plans into clear, actionable steps for both technical and non-technical stakeholders 
• Excellent communication and collaboration skills, with the ability to partner with various stakeholders across technical and non-technical teams.
• Strong leadership and organizational skills, with the ability to manage multiple projects simultaneously.

Educational Requirements:

• A Bachelor’s degree in a technology-related field of study is required. A Master’s degree is preferred.

EEO

Equal Opportunity Employer: Race/Color/Sex/Sexual Orientation/Gender Identity/Religion/National Origin/Disability/Vets