Remote Lead Data Architect for AI-Driven Cancer Data

McKesson’s Corporate

Town of Texas (WI)

Remote

USD 140,000 - 210,000

Full time

4 days ago

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Job summary

Sarah Cannon Research Institute (SCRI), a McKesson subsidiary, is seeking a Lead Data Architect to drive our data strategy across products and clinical research. You will design scalable data infrastructures to support oncology trials and healthcare delivery, aligning data governance with HIPAA and business goals.

The role is remote based in the US with collaboration across teams, data engineers, scientists, and IT.

Qualifications

Minimum 7 years in enterprise data architecture including data requirements collection, data architecture analysis, evaluation, documentation, data modeling, design, development, model validation, and data integration in modern data platforms (e.g., Azure, Databricks, Snowflake).
Bachelor’s Degree in Computer Science, Information Systems, Engineering, or equivalent experience.

Responsibilities

Design, build, and manage the organization's data infrastructure.
Create the blueprint for how data is collected, stored, processed, and used, ensuring it's accessible, reliable, and secure.
Design the structure and systems that allow the organization to effectively manage and use its data, ensuring it is a valuable asset for decision-making and business operations.
Create and maintain documentation for database architecture, procedures, and standards.
Data Strategy: Develop and implement a comprehensive data strategy aligned with business goals.
Data Modeling: Design conceptual, logical, and physical data models that represent the structure and relationships of data within the organization.
Data Architecture: Create and maintain the overall data architecture framework, including databases, data warehouses, data lakes, and other data systems.
Data Mapping: Develop and implement data mapping rules to transform data from source systems to target systems, ensuring data accuracy and consistency.
Data Integration: Design and implement processes for integrating data from various sources, ensuring data consistency and accuracy.
Data Governance: Establish data governance policies and procedures to ensure data quality, security, and HIPAA compliance across all data systems and platforms.
Technology Evaluation: Evaluate and recommend data management technologies and tools to support the organization's data needs.
Problem Solving: Identify and resolve data-related issues, ensuring data integrity and reliability.
AI Solutions: Design and implement architectures that seamlessly integrate AI models and algorithms with existing data infrastructures.
Collaboration: Work closely with stakeholders, including business analysts, data scientists, and IT professionals, to understand data requirements and translate them into technical solutions.
Influence projects to make decisions related to data strategies including data quality, data architecture and data management best practices.