Lead Data Architect

McKesson•Irving, TX

2d•$147,800 - $246,400•Hybrid

About The Position

MomentimRx is advancing a modern healthcare data platform and digital products to accelerate innovation and improve patient health outcomes. The Lead Data Architect will lead the design and execution of enterprise-scale, secure, and scalable data solutions, shaping the next-generation healthcare data capabilities at McKesson. This role partners with business and technology leaders to define architectural strategy, drive interoperability, and enable AI/ML and analytics-ready environments.

Requirements

10+ years in technology roles with significant architecture experience in complex enterprises.
Expertise in Azure, Databricks, multi-cloud architectures, data lakes/lakehouses, real-time streaming, Unity Catalog, and modern data governance frameworks.
Strong proficiency in data modeling techniques (conceptual, logical, physical) and experience with data modeling tools.
Advanced data modeling, normalization, ETL/ELT pipeline design, and batch/streaming architectures.
Experience with relational databases (SQL Server, Oracle, PostgreSQL) and NoSQL databases (MongoDB, Cassandra, DynamoDB).
Familiarity with data governance frameworks, data quality management, and master data management (MDM) principles.
Integration of external data sources, SFTP-based ETL, and real-time pipelines.
Experience integrating metadata and lineage into enterprise data governance platforms (e.g., Alation) to enable discoverability, auditability, and data quality across the organization.
SOA modernization, service-based architectures, OAuth2/OIDC/SMART/RBAC/ABAC; deep understanding of healthcare data standards (HL7, FHIR, ICD-10) and HIPAA compliance.
Architecting ML/AI-ready environments and real-time analytics platforms.
CI/CD pipelines, automated deployment, GitHub version control.
EMR systems, claims processing, MDM, and IQVIA or similar healthcare datasets.
Ability to align diverse stakeholders around a clear and compelling technical vision.
Strong analytical skills to solve complex, cross-functional enterprise challenges.
Proven ability to balance business priorities, technical constraints, and risk management.
Excellent communicator capable of influencing audiences from engineers to senior executives.
Experience participating in and leading architecture review boards and defining reusable enterprise patterns.
Strategic mindset with the ability to guide long-term platform and capability evolution.
Demonstrates collaboration, accountability, and a team-first mindset.
Deep understanding of the healthcare industry, enterprise risk management, and regulatory considerations.
Proven ability to partner cross-functionally with Compliance, Privacy, and Governance stakeholders to drive enterprise data strategy and embed policy-driven governance controls into architecture designs.

Nice To Haves

Startup experience (2+ years) strongly preferred.

Responsibilities

Define technical strategy, architecture standards, and long term roadmap; lead design and planning for new platforms, products, and services; establish enterprise-wide best practices and reusable patterns.
Oversee architectural execution of enterprise scale data solutions; evaluate emerging technologies and recommend innovative, business-aligned solutions; mentor teams on modern architecture and cloud-native practices.
Provide technical governance through architecture review boards and standards enforcement; ensure compliance with healthcare regulations and enterprise security standards; embed data security, privacy, and regulatory compliance in all data architecture designs.
Partner closely with Compliance, Privacy, and Enterprise Governance teams to define and execute a holistic data strategy aligned with regulatory requirements and enterprise risk posture.
Work in close collaboration with Data Governance organizations to operationalize metadata management and lineage by integrating enterprise data assets with governance platforms such as Alation.
Ensure consistent capture, enrichment, and stewardship of metadata across data platforms to support discoverability, lineage, data quality, and auditability.
Develop and maintain comprehensive documentation of data architecture, data dictionaries, and data flow diagrams; ensure consistency, traceability, and quality across platforms.