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. Reporting to the AIM28 Finance Lead, this role will design and own end-to-end data models, ETL pipelines, dashboards, and automation solutions that transform complex, fragmented data into trusted insights for executives and program leaders. In addition, this role will support other strategic projects as required.

The ideal candidate combines deep technical expertise in SQL, data modeling, BI visualization, automation, and AI-enabled analytics with strong business and finance acumen. Operating as the sole analytics builder within the AIM28 Finance team, 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.

The Senior Business Intelligence Analyst will lead the development of business intelligence, data visualization, analytics, and reporting automation for the AIM28 program and other strategic projects within U.S. Pharmaceutical Distribution (USPD). AIM28 is McKesson6#39;s AI-enabled transformation program, organized around a portfolio of Big Bets, each accountable for delivering measurable business outcomes. This role reports directly into the Sr. Director, Finance (AIM28). This role 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 source material varies: some data arrives raw and needs business logic before anyone can use it, some lives in manual files, and some is already visualized in dashboards the business teams built for themselves.

You will architect an AI-ready analytics and reporting model that pairs traditional dashboards with AI capabilities such as automated narratives and conversational agents, so stakeholders can self-serve and get to insight faster. Where data is raw, you will establish the transformation logic that makes it consumable. Where reporting lives in manual files or scattered dashboards, you will integrate and standardize it into Big Bet and AIM28 program-level solutions. This is a highly visible individual contributor role. We are looking for a builder with deep SQL proficiency, strong knowledge of data design, and the visualization and storytelling skill to brief senior executives. Expect problems to show up half-defined; your job is to turn them into reliable numbers and clear answers.

This role works under the direction of the AIM28 Finance Lead partnering with, Big Bet teams, the transformation office, and McKesson Technology to codify KPI definitions and benefit methodologies into governed data models. The goal is one version of the truth across the program.

This is a pure individual-contributor role embedded in Finance, not a role inside a BI or data engineering organization. There is no bench of other BIEs, analytics engineers, or data engineers to hand design, build, or troubleshooting off to, you 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 FP6A team.

• Design, build, and maintain the AIM28 program-level and Big Bet-level dashboards that track KPIs, benefits, investments, milestones, and value realization. These dashboards are the single source of truth for program performance.

• Develop executive-facing reporting for monthly Steering Committee meetings, business reviews, and leadership updates. Translate complex program data into insights leaders can act on.

• Automate recurring benefit and program reporting end to end. Replace 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 that surface insights before anyone asks for them.

• Build and maintain scalable ETL/ELT pipelines that extract, transform, and clean large, multi-dimensional datasets from multiple sources. These pipelines power the automated Big Bet and AIM28 program-level reporting.

• Ingest and integrate data from Big Bet teams into harmonized, consumable datasets and data models. Sources include raw operational data, manually maintained files, and dashboards the business built for itself.

• Establish the business logic that converts raw source data into certified reporting. Document the logic, lineage, and metric definitions well enough that someone else could audit or maintain the work.

• 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, so measurement stays consistent across Big Bets.

• Develop and monitor the KPIs that drive business decisions. Implement data-quality checks, reconciliation, and anomaly detection to protect trust in the numbers.

• Conduct deep-dive analyses of KPI movements, benefit variances, and program performance questions. Identify root causes and bring conclusions and recommendations to senior leadership.

• Answer ad hoc analytical questions from program leadership and Big Bet teams. When sources disagree, triangulate across them to validate data fidelity.

• Enable governed self-service analytics for program and Big Bet stakeholders, so fewer questions turn into ad hoc requests or one-off analyses.

• Own delivery end to end, from requirements and solution design through build, UAT, and adoption. Work with business stakeholders, data engineering, and IT to keep solutions compliant with enterprise data standards.

• Prioritize the reporting and automation backlog by business impact and effort. Keep stakeholders current on the roadmap and the trade-offs behind it.

• Set the standard for analytics practice (data integrity, documentation, versioning) across Big Bet teams, and coach business analysts who build their own reporting.

• A builder6#8217;s mindset. You take loosely defined problems, work out the business intent behind them, and deliver working solutions end to end: requirements, data model, pipeline, dashboard, adoption.

• Comfort with ambiguity and imperfect data. Raw extracts, manual spreadsheets, inconsistent definitions, and shifting requirements are the starting point here, not a blocker.

• Business acumen and financial literacy. You understand core finance concepts, ROI, forecasting, variance-to-plan, business-case logic well enough to hold your own in a finance conversation, not just translate what finance tells you. You have worked directly with a finance or FP6A team before, and you can sit with finance and business partners, understand benefit logic and business cases, and translate between data and business needs.

• Comfort as a standalone technical IC. This role sits in Finance, not in a BI or data engineering organization. There is no dedicated BIE team, data engineering bench, or technical manager to lean on for design review, code review, or troubleshooting, you are the technical function for AIM28 reporting. You are comfortable being the only builder in the room and owning decisions end to end.

• A KPI and automation track record. You have developed and monitored KPIs that drive business decisions, and you have built automated reporting systems that replaced manual processes.

• Executive presence. You have experience communicating and presenting analytical results to senior leadership, in writing and in person.

• Ownership and judgment. You validate your own outputs, make pragmatic build-versus-reuse trade-offs, and earn trust by being right about the numbers.

• Organization and prioritization. You manage multiple concurrent workstreams across Big Bets without letting deliverables or timelines slip.

• Curiosity about AI. You are interested in applying GenAI and agents to reporting, and ideally you have already experimented with them.

• Expert SQL: complex query development, window functions, and performance tuning on large, multi-dimensional datasets from multiple sources. You write queries that need little post-processing.

• Data modeling and design: dimensional and star-schema modeling, semantic model design, and harmonized dataset construction. You can evaluate an end-to-end data design for its strengths and weaknesses.

• 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 for data transformation and automation.

• Workflow automation: Power Automate, Power Apps, or comparable low-code tooling for reporting workflows. Exposure to modernizing legacy RPA into AI-enabled solutions is a plus.

• Analytics foundations: descriptive statistics, trend and variance analysis, and metric calibration, plus enough familiarity with inferential methods to know when to use them.

• AI-enabled analytics (differentiator): applying LLM and GenAI capabilities to BI: automated narrative generation, conversational analytics, and agent-ready semantic layers.

• Executive tooling: advanced Excel and PowerPoint for analysis and executive storytelling.

Degree or equivalent and typically requires 7+ years of relevant experience. Fewer years required if candidate holds a relevant Master6#8217;s qualification.

Education

Bachelor6#8217;s degree in Computer Science, Engineering, Mathematics, Statistics, Economics, Information Systems, or a related quantitative field, or equivalent experience. Advanced degree preferred.

• 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 P6L mechanics) and direct experience partnering with a finance or FP6A 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.

• 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 experi