Principal Data Modernization Engineer

McKesson•Columbus, OH

1d•Remote

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. The Principal Data Modernization Engineer is a senior technical individual contributor who provides trusted technical leadership across data modernization efforts within CoverMyMeds’ (CMM) Data Technology organization. This role operates across transactional engineering, analytical engineering, platform, and data architecture to ensure modernization initiatives are cohesive, technically sound, and aligned to long-term strategy. This role also partners closely with analytics teams to ensure the data platforms and capabilities being modernized effectively support analytical use cases and evolving business needs. Reporting directly to the Senior Director of Data Technology, this role acts as a technical delegate and strategic partner, supporting leadership by shaping direction, providing clarity in complex situations, and guiding high-impact modernization decisions. While this role does not own day-to-day delivery or people management, it plays a critical role in helping the organization move forward with confidence on its most important technical choices. The role exists to identify opportunities for alignment, frame complex problems, bring the right leaders together, and guide technical decisions, stepping in where clarity is needed and stepping back once direction and ownership are established. Our preferred candidate will reside in Columbus, OH, or one of our other hub locations of Atlanta GA or Irving TX. Position allows for primarily working from home, with occasional in-office time. We may consider a well-qualified candidate based not located in one of the above hub areas. At this time, we are not able to offer sponsorship for employment visas. We're unable to consider individuals currently on H1B, F-1 OPT, STEM OPT, or any other visa status that would require future sponsorship. Candidates must be authorized to work in the United States on a permanent basis without the need for current or future sponsorship.

Requirements

Bachelor's degree in Computer Science, Information Systems, or equivalent experience and typically requires 13+ years of relative experience in data engineering, data platforms or large-scale systems
Experience partnering with analytics or reporting teams to ensure data platforms, models and pipelines effectively support analytical workloads and evolving analytical needs
Strong Technical understanding of: Transactional and analytical data systems Data ingestion, integration, and synchronization patterns Cloud-based data platforms, specifically Databricks Data modeling and domain-oriented data design
Demonstrated ability to influence technical direction through credibility, collaboration and clear communication
Experience working effectively in complex, multi-team technical environments

Nice To Haves

Experience contributing to or guiding enterprise-scale data modernization initiatives
Strong facilitation and communication skills; able to create clarity in ambiguous situations
Ability to balance long-term architectural thinking with practical delivery realities
Recognized as a trusted technical advisor to senior leaders and peers

Responsibilities

Senior Technical Guidance & Decision Stewardship • Act as the Senior Director’s technical delegate for data modernization, providing trusted technical guidance across Data Technology
Guide and shape high-impact modernization decisions that span multiple teams or domains
Provide clarity and direction in complex or ambiguous modernization scenarios, enabling leaders and teams to move forward with confidence
Ensure key technical decisions are clearly articulated, shared, and consistently applied across Data Tech Problem Discovery & Technical Framing • Proactively identify gaps, inefficiencies, or unclear ownership in data modernization (e.g., ingestion ownership, platform responsibilities, data flow boundaries)
Develop a strong understanding of current-state systems, constraints, and dependencies across teams
Translate ambiguity into clear problem statements, decision options, and technical recommendations Cross-Team Collaboration & Alignment • Partner with Engineering and Architecture Directors to align on modernization approaches and technical direction
Bring together the appropriate leaders and senior engineers to explore options, assess trade-offs, and align on paths forward
Foster constructive, outcome-focused discussions that balance technical rigor with practical delivery considerations
Partner with analytics teams to understand analytical usage patterns, performance needs, and downstream considerations that should inform data platform and modernization decisions
Ensure data modernization decisions appropriately support analytics scalability, usability, and long-term analytical flexibility Modernization Pattern Application & Consistency • Guide the application of data modernization patterns and approaches across transactional, analytical, platform, and modeling work, with consideration for downstream analytical consumption and performance needs
Help teams consistently interpret and apply data architecture standards in real-world modernization scenarios
Identify opportunities to simplify, standardize, or improve how modernization patterns are used across Data Tech Risk Awareness & Strategic Insight • Maintain visibility into emerging modernization risks, architectural drift, or areas of growing technical debt
Surface insights and recommendations to leadership early, supporting informed decision-making
Help reduce reliance on informal heroics by promoting clear, repeatable approaches to technical decision-making