Lead Data Architect & Engineer

McKesson is an impact-driven, Fortune 10 company that touches virtually every aspect of healthcare. We are known for delivering insights, products, and services that make quality care more accessible and affordable. Here, we focus on the health, happiness, and well-being of you and those we serve - we care.

What you do at McKesson matters. We foster a culture where you can grow, make an impact, and are empowered to bring new ideas. Together, we thrive as we shape the future of health for patients, our communities, and our people. If you want to be part of tomorrow's health today, we want to hear from you.

The Lead Data Engineer / Architect is a senior individual contributor on McKesson's Provider Revenue Operations team. This role owns the data architecture, pipelines, models, and automation behind revenue and performance analytics.

The role builds and supports the data used for sales performance dashboards, month-end and quarter-end close, payment and commission validation, and leadership reporting. It works closely with technical teams, business partners, and leaders to deliver scalable solutions that meet current needs and support future growth.

This person serves as the primary technical lead for Revenue Operations data architecture and development, including cloud platforms, system integrations, automation, and platform migrations.

Much of this work currently depends on manual processes and individual knowledge. This role will replace those dependencies with automated, reusable data infrastructure so that reporting is accurate, consistent, and independent of any one person.

An AI-minded approach is core to the role. Modern AI capabilities such as retrieval-augmented generation (RAG), large language models, and agentic workflows are applied where they measurably speed up or sharpen revenue intelligence, along with building the data foundations that make those use cases possible. AI is a tool this work relies on when it fits, not the primary mandate.

• 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.

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. Relevant advanced education may offset some experience.

• 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,…