Senior Business Intelligence & Automation Analyst

McKesson•Irving, TX

2d•$114,200 - $190,400•Hybrid

About The Position

McKesson is an impact-driven, Fortune 10 company focused on making quality care more accessible and affordable. We foster a culture of growth, impact, and empowerment. The Senior Business Intelligence Analyst is a highly visible, hands-on individual contributor responsible for building and scaling AIM28's analytics, reporting, and value-realization measurement capabilities. This role will design and own end-to-end data models, ETL pipelines, dashboards, and automation solutions that transform complex data into trusted insights for executives and program leaders. The ideal candidate combines deep technical expertise in SQL, data modeling, BI visualization, automation, and AI-enabled analytics with strong business and finance acumen. This person will create the program's single source of truth for KPI, benefit, investment, and value-realization reporting while driving self-service analytics, reporting automation, and AI-powered insights across the AIM28 portfolio. This role reports directly into the Sr. Director, Finance (AIM28) and is the hands-on builder who turns Big Bet data into trusted, automated benefit and value-realization reporting for program leadership and senior executives. The role architects an AI-ready analytics and reporting model that pairs traditional dashboards with AI capabilities such as automated narratives and conversational agents. This is a pure individual-contributor role embedded in Finance, not a role inside a BI or data engineering organization. You will own the full stack yourself, end to end. Because the role sits in Finance rather than Technology, you should also be comfortable working fluently in financial concepts: ROI, forecasting, variance-to-plan, and business-case logic, gained through direct experience partnering with a business finance or FP&A team.

Requirements

A builder’s mindset: take loosely defined problems, work out the business intent, and deliver working solutions end to end.
Comfort with ambiguity and imperfect data.
Business acumen and financial literacy: understand core finance concepts, ROI, forecasting, variance-to-plan, business-case logic.
Comfort as a standalone technical IC, owning decisions end to end without a dedicated BI or data engineering organization to lean on.
A KPI and automation track record: developed and monitored KPIs, built automated reporting systems.
Executive presence: experience communicating and presenting analytical results to senior leadership.
Ownership and judgment: validate own outputs, make pragmatic build-versus-reuse trade-offs.
Organization and prioritization: manage multiple concurrent workstreams.
Curiosity about AI: interested in applying GenAI and agents to reporting.
Expert SQL: complex query development, window functions, and performance tuning.
Data modeling and design: dimensional and star-schema modeling, semantic model design, and harmonized dataset construction.
BI and visualization: advanced Power BI and Tableau, including data modeling, DAX, executive-grade dashboards, and workspace and access management.
ETL/ELT and pipelines: hands-on experience programming to extract, transform, and clean large datasets, and building scheduled, monitored pipelines in tools such as Databricks, Microsoft Fabric, Azure Data Factory, or Snowflake.
Scripting for automation: proficiency in Python or a similar language.
Workflow automation: Power Automate, Power Apps, or comparable low-code tooling.
Analytics foundations: descriptive statistics, trend and variance analysis, and metric calibration.
Executive tooling: advanced Excel and PowerPoint.
Degree or equivalent and typically requires 7+ years of relevant experience. Fewer years required if candidate holds a relevant Master’s qualification.
Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, Economics, Information Systems, or a related quantitative field, or equivalent experience.
7+ years of experience as a Business Intelligence Engineer, Data Analyst, Analytics Engineer, Data Engineer, or a related occupation delivering BI, analytics, and reporting solutions.
Deep SQL proficiency and strong knowledge of data design and data modeling.
Expert-level dashboard development in Power BI (or equivalent) for executive audiences.
Experience programming to extract, transform, and clean large datasets from multiple, disparate sources, including raw and manually maintained data.
Experience designing and implementing custom, automated reporting systems using automation and scripting tools such as Python and Power Automate.
Track record of developing and monitoring KPIs to drive business decisions, ideally including benefit or value-realization tracking for strategic programs.
Demonstrated ability to establish business logic and metric definitions that turn raw data into trusted, consumable reporting.
Experience communicating and presenting analytical results to senior leadership.
Strong analytical skills with the ability to translate data into actionable insights.
Demonstrated organizational and prioritization abilities.
Ability to navigate ambiguity and deliver independently in a fast-paced transformation environment.
Working knowledge of core finance concepts (ROI, forecasting, variance-to-plan, business-case and P&L mechanics) and direct experience partnering with a finance or FP&A team to translate financial methodology into data logic.
Demonstrated ability to operate as the sole technical IC on a team, without a dedicated BI or data engineering organization to lean on for support, review, or troubleshooting.

Nice To Haves

Experience supporting large-scale transformation programs, PMO or value-realization analytics, or benefit tracking for strategic initiatives.
Hands-on experience with Databricks, Microsoft Fabric, Azure data services, or Snowflake.
Experience embedding GenAI or AI-agent capabilities into analytics solutions, such as automated narratives, conversational BI, or semantic layers for agents.
Exposure to SAP/ERP data and enterprise source systems, including S/4HANA migration environments.
Experience with the theory and practice of design of experiments and statistical analysis of results.
Experience enabling governed self-service analytics and establishing data and reporting SLAs.
Healthcare, pharmaceutical distribution, or other regulated-industry experience is a plus.
Agile delivery experience and strong documentation habits.
PowerPoint creation and executive storytelling.

Responsibilities

Design, build, and maintain the AIM28 program-level and Big Bet-level dashboards that track KPIs, benefits, investments, milestones, and value realization.
Develop executive-facing reporting for monthly Steering Committee meetings, business reviews, and leadership updates.
Automate recurring benefit and program reporting end to end, replacing manual, file-based processes with scheduled data flows that are validated and monitored.
Find and implement automation opportunities across reporting workflows, including AI capabilities such as automated narratives and summaries.
Build and maintain scalable ETL/ELT pipelines that extract, transform, and clean large, multi-dimensional datasets from multiple sources.
Ingest and integrate data from Big Bet teams into harmonized, consumable datasets and data models.
Establish the business logic that converts raw source data into certified reporting and document the logic, lineage, and metric definitions.
Design semantic models and curated datasets that people consume today through dashboards and self-service, and that AI agents can consume over time.
Partner with the AIM28 Finance lead to codify KPI definitions and benefit-realization methodologies into governed data models.
Develop and monitor the KPIs that drive business decisions, implementing data-quality checks, reconciliation, and anomaly detection.
Conduct deep-dive analyses of KPI movements, benefit variances, and program performance questions, identifying root causes and bringing conclusions and recommendations to senior leadership.
Answer ad hoc analytical questions from program leadership and Big Bet teams.
Enable governed self-service analytics for program and Big Bet stakeholders.
Own delivery end to end, from requirements and solution design through build, UAT, and adoption.
Prioritize the reporting and automation backlog by business impact and effort.
Set the standard for analytics practice (data integrity, documentation, versioning) across Big Bet teams, and coach business analysts who build their own reporting.