Lead Data Engineer

McKesson•Alpharetta, GA

About The Position

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. Summary The Lead Data Engineer is a senior individual contributor within McKesson’s Decision Intelligence organization, responsible for leading the design, delivery, and operationalization of scalable data engineering and analytics solutions across the PSaS Business Unit. This role serves as a technical leader and player‑coach, combining deep hands‑on engineering with solution leadership, mentorship, and cross‑functional influence.

Requirements

Recognized expertise in data engineering and analytics within large, enterprise environments.
Strong ability to independently lead complex technical initiatives with minimal oversight.
Proven experience influencing technical direction and mentoring other engineers without formal people‑management responsibility.
Deep understanding of data architecture, ETL/ELT patterns, and large‑scale data processing.
Strong stakeholder communication and collaboration skills.
Advanced proficiency in SQL, Python, and scripting.
Strong hands‑on experience with: Databricks, Snowflake, Azure Data Factory Confluent Kafka / Azure Event Hub PySpark and analytical SQL Power BI and/or Tableau Apache Airflow, dbt, Alation
Strong understanding of data modeling, metadata, lineage, and data quality practices.
Experience with cloud platforms (SaaS, PaaS, IaaS).
Experience implementing automation and Infrastructure as Code (IaC).
Ability to support advanced analytics and ML enablement (non‑pure DS focus).
Degree or equivalent and typically requires 10+ years of relevant experience.

Nice To Haves

Experience leading enterprise‑scale or BU‑wide data initiatives.
Exposure to real‑time, event-driven data architectures.
Familiarity with data governance programs and stewardship responsibilities.
Experience experimenting with emerging data and AI technologies.
Background in highly regulated or complex business environments.

Responsibilities

Lead end‑to‑end technical delivery for complex data engineering initiatives or domains.
Serve as a senior technical point of contact for data engineering within cross‑functional squads.
Design and build scalable, reliable batch and real-time data pipelines across internal and external systems.
Lead solution design and influence architectural decisions in collaboration with Data Architects and enterprise teams.
Define and uphold engineering standards, patterns, and best practices aligned with enterprise data strategy.
Mentor and guide data engineers through design reviews, code reviews, and technical coaching.
Ensure quality, reliability, performance, and long‑term maintainability of data products.
Partner with Product Managers, Data Scientists, Analysts, and business stakeholders to translate analytical requirements into scalable solutions.
Drive an automation‑first and reusability-focused mindset across data engineering solutions.
Oversee testing strategies, production readiness, observability, and operational stability of data pipelines.
Proactively identify technical debt and lead remediation efforts.
Enable advanced analytics and ML use cases through optimized data models and pipelines.
Communicate technical designs, tradeoffs, risks, and outcomes clearly to stakeholders.