Lead Data Architect & Engineer

McKesson is an impact-driven healthcare company focused on making quality care more accessible and affordable. The Lead Data Architect & Engineer owns the data architecture, pipelines, models, and automation supporting revenue and performance analytics. The role leads cloud data development, integrations, reporting infrastructure, and applied AI capabilities for Revenue Operations.

Responsibilities:

• Design, build, and support a cloud-based data environment for structured, semi-structured, and unstructured revenue and performance data
• Define Revenue Operations data architecture and cloud data models based on business, analytical, operational, security, and regulatory needs
• Own the full data lifecycle, from data collection and transformation through dashboards and reports
• Automate the data flows supporting month-end and quarter-end close
• Build consistent logic for payment, commission, quota, and attainment validation
• Develop conceptual, logical, and physical data models for operational and analytical use
• Design ETL and ELT pipelines, APIs, real-time integrations, and event-driven solutions
• Integrate data from Salesforce, Outreach, finance systems, and other business platforms
• Build fact tables, dimensions, and semantic layers that provide a governed source of truth for Power BI and Tableau
• Ensure every data product is accurate, reliable, scalable, and maintainable
• Serve as the primary technical and architecture contact for revenue and performance analytics
• Define standards for data, integration, backend, frontend, and automation solutions
• Establish design patterns for cloud, on-premises, and hybrid environments
• Maintain clear documentation showing how data is stored, processed, integrated, secured, and accessed
• Evaluate and recommend technologies that improve development, automation, data management, performance, and support
• Promote automation, consistency, and reuse across analytics and financial close processes
• Build governed data foundations that allow revenue and performance data to be used safely by AI solutions, including RAG pipelines
• Apply large language models, vector search, and agentic workflows to practical Revenue Operations needs when they deliver measurable value
• Develop use cases such as natural-language data queries, automated variance explanations, anomaly detection, and self-service insights
• Apply responsible AI standards, including grounding, guardrails, privacy, security, and auditability
• General office demands
• Must have the ability to travel up to 20% of the time

Requirements:

• Degree or equivalent and typically requires 7+ years of relevant experience
• Bachelor's degree or an equivalent combination of education and experience
• Typically requires 7+ years of experience in data engineering, analytics engineering, or data and application architecture
• Expert knowledge of enterprise data pipelines, ETL and ELT processes, and data models supporting sales, revenue, or financial reporting
• Advanced SQL, Python, and scripting skills for complex queries, performance optimization, and large-scale data processing
• Deep knowledge of conceptual, logical, and physical data modeling, including metadata, lineage, and enterprise data standards
• Strong hands-on experience with Databricks, Snowflake, and Azure Data Factory
• Experience integrating Salesforce, sales engagement platforms such as Outreach, finance systems, and other enterprise applications
• Experience designing APIs, real-time integrations, and event-driven solutions
• Experience building bots, system integrations, and reusable automation components
• Hands-on experience with PySpark and Power BI or Tableau
• Experience with cloud, on-premises, and hybrid platforms, including SaaS, PaaS, IaaS, and Infrastructure as Code
• Experience building data architecture for sales performance, revenue, commission, or incentive reporting
• Ability to independently lead complex data and architecture initiatives with minimal oversight
• Ability to explain technical work and recommendations clearly to sales and operations leaders
• General office demands
• Must have the ability to travel up to 20% of the time
• Experience supporting month-end or quarter-end close and financial or revenue reconciliation is strongly preferred
• Experience with process automation, enterprise applications, or platform migrations is preferred
• Experience applying emerging data and AI technologies to business problems is preferred
• Experience in healthcare or another regulated industry is strongly preferred