Director, Technical & Platform Product Management

McKesson•Irving, TX

2d•$143,300 - $238,900•Hybrid

About The Position

The Director, Technical & Platform Product Management will lead strategy, development, launch, and adoption of data, analytics, and related technology platform capabilities for McKesson’s pharmacy technology business. The role partners cross-functionally to determine what data consumers need, how they will consume it, and which business capabilities it must support. It owns the data product backlog and translates needs into capabilities, epics, stories, acceptance criteria, and roadmap priorities, working closely with engineering and other product team members. Success is measured by data freshness, completeness, quality, availability, adoption, delivery efficiency, and measurable business value. The position defines the data platform vision, target consumers, value propositions, roadmap, and build-versus-buy decisions. It leads discovery and validation of data needs and feasibility; shapes reusable data ingestion, pipeline, modeling, API, event, extract, and data-product capabilities; and sets priorities while balancing business value, dependencies, risk, security, technical debt, and cost. The role also defines legacy data migration requirements and consumer onboarding, documentation, and feedback strategies. It establishes and monitors data and platform expectations, including quality, reliability, latency, adoption, cost, and consumer experience. In partnership with engineering, architecture, security, and data teams, it guides governance and lifecycle decisions involving lineage, cataloging, stewardship, privacy, retention, access controls, versioning, migration, and deprecation. It communicates trade-offs and aligns technical and business stakeholders without prescribing engineering implementation. The position may be hybrid. Candidates located in the Dallas–Fort Worth area may be expected to work onsite at the Las Colinas office at least two days per week, with remaining days remote; specific onsite days may depend on team needs and business priorities. Some travel may be required for cross-site collaboration and customer or partner engagements. McKesson does not provide immigration support or sponsorship for this role; applicants must be authorized to work in the United States without current or future employer sponsorship.

Requirements

Degree or equivalent and typically 10+ years of relevant experience; fewer years may be required with relevant Master’s or Doctorate qualifications.
Bachelor’s degree in computer science, engineering, information systems, a related technical discipline, or equivalent relevant professional experience.
10+ years of progressive product management experience supporting enterprise software, SaaS, platform, infrastructure, or developer-facing technology products.
Experience leading at least one technically complex platform or product from initial concept through production launch and adoption.
Experience defining product strategy, roadmaps, requirements, priorities, success measures, and adoption plans for technical data products such as a data lake, data warehouse, or enterprise data warehouse.
Experience working directly on data migration with software engineers and architects on distributed systems, APIs, integrations, data models, cloud architecture, scalability, reliability, security, or observability.
Hands-on experience with at least one of: reviewing API specifications, querying data using SQL or Python, evaluating logs or telemetry, or developing lightweight technical prototypes.
Experience defining and using measurable platform or service indicators such as adoption, availability, latency, throughput, error rates, recovery objectives, delivery cycle time, infrastructure cost, or developer experience.
Experience managing platform interfaces through versioning, backward compatibility, migration planning, dependency management, or deprecation.
Experience with Databricks and/or Snowflake environments and application of their AI/ML features.

Nice To Haves

Previous experience in software engineering, systems architecture, technical leadership, data engineering, or infrastructure engineering.
Experience developing products for technical users, including APIs, SDKs, developer portals, self-service workflows, integration standards, documentation, or CI/CD tooling.
Experience with public cloud platforms, containerized applications, event streaming, infrastructure as code, observability platforms, identity services, or developer platforms.
Experience with AI or machine learning platforms, large language model orchestration, vector databases, responsible AI practices, or production data pipelines.
Experience launching pilot or beta programs with defined user groups, measurable success criteria, operational readiness requirements, and data-informed launch decisions.
Experience with marketplace or multi-sided platform products.
Advanced degree in computer science, engineering, data science, business, or a related discipline.

Responsibilities

Define the data platform strategy and roadmap, including capability vision, target consumers, value propositions, roadmap priorities, and build-versus-buy decisions.
Lead data product discovery by partnering with data teams to define data needs, consumption methods, and supported business capabilities, and validate needs and feasibility with engineering and architecture.
Shape reusable data capabilities by defining requirements for ingestion, batch and streaming pipelines, change data capture, transactional and analytical data models, APIs, events, extracts, and data products.
Own the data product backlog and translate consumer needs into capabilities, epics, stories, acceptance criteria, and executable roadmap priorities.
Define platform data deliverables while balancing business value, dependencies, risk, security, technical debt, and cost.
Define legacy-platform data migration requirements, including transformation, mapping, enrichment, and reconciliation; develop consumer onboarding, documentation, and feedback strategies.
Establish measurable expectations for data freshness, completeness, quality, and availability; monitor adoption, pipeline reliability, latency, cost, and consumer experience, and use telemetry and feedback to guide roadmap decisions.
Partner with engineering, architecture, security, and data teams on data quality, governance, lineage, cataloging, stewardship, PHI/PII, retention, security, access controls, versioning, migration, and deprecation.
Communicate data platform trade-offs to engineering, product, customers, and executives, and align cross-functional decisions on business capabilities, consumer needs, architecture, reliability, security, and cost without prescribing engineering implementation.