{"id":2978942,"url":"https://alion.io/job/mckesson-lead-solution-architect-customer-analytics-edw-ai","title":"Lead Solution Architect, Customer Analytics, EDW & AI","company":{"id":4393,"name":"McKesson","domain":"mckesson.com","url":"https://alion.io/company/mckesson","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"B","score":80,"open_postings":33,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":12,"computed_at":"2026-10-23T05:54:15Z"}},"role":"Solutions","role_family":"Solutions","seniority":"lead","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mississauga, Canada"],"countries":["CA"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":76000,"max_usd":149000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":26},"experience_years_min":10,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AutoGen","optional":false},{"name":"Chroma","optional":false},{"name":"CrewAI","optional":false},{"name":"Databricks","optional":false},{"name":"Embeddings","optional":false},{"name":"Gmail","optional":false},{"name":"LangChain","optional":false},{"name":"LangGraph","optional":false},{"name":"Pinecone","optional":false},{"name":"Power BI","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"RAG","optional":false},{"name":"SQL","optional":false},{"name":"Weaviate","optional":false}],"status":"live","first_seen_at":"2026-07-21T00:00:00Z","employer_posted_date":"2026-07-21","last_verified_at":"2026-10-23T07:59:57Z","board_verified":true,"closed_at":null,"days_open":95,"trust":{"level":"stale","repost_count":0,"flags":["stale"],"days_open":94},"description":"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.\nWhat 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.\nJob Title: LeadSolutionArchitect-CustomerAnalytics,EnterpriseDataWarehouse&AI\nPositionSummary\nTheLeadSolutionArchitect-CustomerAnalytics,EnterpriseDataWarehouse&AI is responsible for establishing enterprise-grade technical architecture that delivers sustained business value across system-wide, mission-critical programs. This role owns architectural components and ensures alignmenttofuture-statetechnologyvision,directsfit-gapanalysis,validatesmigrationplans,andevaluatestechnologyplatformsandarchitecturalpatternstoensuresolutionsmeetrigoroussecurity,performance,reliability,compliance,andoperabilityexpectations.\nThis role will focus on customer-facing analytics, enterprise data warehouse integrations, reporting products, APIs, dashboards, semantic models,andAI-enableddataproducts.Thearchitectwillguidefull-stackengineeringandEDWteamstodesignscalable,secure,andreliableplatformsthatdeliveractionableinsightstocustomersthroughdashboards,APIs,semanticlayers,andintelligentAI-poweredexperiences.\nThe successful candidate will bring deepexperienceindataanalytics,large-scaleEDWintegrations,cloud-nativearchitecture,softwaredelivery,andenterpriseAIsolutions,includingRetrieval-AugmentedGeneration,AgenticAIframeworks,LLMorchestration,vectorsearch,AIAPIs,andAzure-basedAIgovernance.\nOperating with a high degree of autonomy, the Lead Solution Architectwillconsultacrossmultipledomains,harmonizeinitiativeswithenterprisearchitecture,setandenforcestandards,providecleartechnicalrecommendationstonon-technicalstakeholders,andadvancemeasurableoutcomesalignedwithMcKesson’sstrategicobjectives.\nKeyResponsibilities\nEnterpriseSolutionArchitecture\nDefineandownthetargetarchitectureforcustomeranalytics,enterprisedatawarehouseintegrations,reportingproducts,APIs,semanticmodels,dashboards,andAI-poweredinsights.\n\nEstablisharchitecturestandardsandreferenceimplementationsacrossSnowflake,Databricks,datamodeling,orchestration/ELT,APIs,front-endconsumption,andcustomer-facingAIcapabilities.\n\nTranslatebusinessrequirementsintoscalablearchitecturedesignsthatalignwithenterprisearchitectureprinciples,businessobjectives,andtechnologystandards.\n\nEvaluatetechnologyoptions,platforms,andarchitecturalpatternstorecommendsecure,scalable,andcompliantsolutioncomponents.\n\nLeaddesignreviewsandprovidearchitecturaldirectionforhigh-impactinitiativesacrossdata,application,AI,andcloudplatforms.\n\nEnsuresolutionsmeetnon-functionalrequirementsforavailability,performance,security,observability,compliance,operability,andcostefficiency.\n\nCustomerAnalytics,EDW&DataPlatformArchitecture\nLeadEDWintegrationarchitecturebydefiningresilientELT/ETLpatterns,datacontracts,lineage,qualitychecks,governancecontrols,andmeasurableserviceexpectations.\n\nModeldataforanalyticsusingfacts,dimensions,semanticlayers,anddataproductsthatsupportBItools,APIs,reportingapplications,andAIconsumptionpatterns.\n\nDriveperformancetuning,partitioning,clustering,caching,andcostgovernanceacrossstorage,compute,andquerylayers.\n\nDesignarchitecturepatternsthatallowstructuredandunstructuredenterprisedatatobesecurelyconsumedbyAIsolutionsthroughgovernedRAGpipelines.\n\nDefinemetadata,lineage,governance,andknowledge-managementstrategiestoimprovetrust,retrievalquality,andresponsegrounding.\n\nArchitectsemanticlayers,dataproducts,andknowledgegraphsthatimprovecontextualretrievalandreasoningacrosscustomeranalyticsplatforms.\n\nAI,RAG&AgenticAIArchitecture\nDefinearchitecturepatternsforAI-poweredanalyticsproducts,includingconversationalanalytics,naturallanguagequeryexperiences,automatedinsightgeneration,intelligentreporting,andautonomousworkfloworchestration.\n\nDesignscalableAgenticAIarchitecturesthatleverageLLMs,multi-agentorchestrationframeworks,toolcalling,memorymanagement,enterpriseAPIs,andsecureexecutionpatterns.\n\nEstablish reference architectures forRAGsolutions,includingdocumentingestion,chunkingstrategy,embeddinggeneration,vectorsearch,semanticretrieval,promptorchestration,grounding,andevaluationframeworks.\n\nLeadintegrationofenterprisedataproductswithAzureOpenAI,AzureAIFoundry,AzureAISearch,vectordatabases,andexternalAIAPIs.\n\nDefine and promote AI governancepracticescoveringresponsibleAI,modelmonitoring,promptsafety,privacy,auditability,explainability,andriskmanagement.\n\nEstablishbestpracticesforpromptengineering,modelevaluation,AIobservability,retrievalqualitymeasurement,agenttesting,andcontinuousmodelimprovement.\n\nEngineeringLeadership&DeliveryEnablement\nPartnerwithfull-stackengineeringteamstoshapeserviceboundaries,APIcontracts,integrationpatterns,andsecuredataconsumptionmodels.\n\nGuideengineeringteamsinbuildingAIservices,copilots,intelligentagents,andconversationalexperiencesintegratedwithcustomer-facinganalyticsproducts.\n\nCreatearchitecturedecisionrecords,solutiondiagrams,APIspecifications,datacontracts,standards,andknowledge-sharingartifacts.\n\nMentorengineers,dataengineers,andarchitectsonarchitecturepatterns,securecoding,testing,reliability,logging,metrics,tracing,alerting,incidentresponse,andoperationalreadiness.\n\nDrivepracticalexecutionfromarchitecturedocumentstoworkingreferenceimplementationsandreusableproduction-gradepatterns.\n\nPartneracrossproduct,datagovernance,security,customersuccess,engineering,andbusinessstakeholderstotranslatebusinessoutcomesintotechnicalroadmaps.\n\nSecurity,Compliance&Governance\nEmbedsecuritybydesign,includingauthentication,authorization,leastprivilege,encryption,secretsmanagement,secureAPIs,andsecuredatasharing.\n\nDefinecontrolsforsecureenterprisedatauseinGenAIapplications,includingvectorstores,embeddings,prompts,LLMinteractions,modeloutputs,andauditability.\n\nSupportgovernanceforPII/PHI,regulatoryrequirements,securitycontrols,andauditreadiness.\n\nLeadarchitecturereviews,riskassessments,threatmodeling,andsecure-by-defaultdesignreviewsfordataandAIproducts.\n\nEnsuresolutionarchitecturedecisionsalignwithenterprisestandards,architectureguidelines,andgovernanceprinciples.\n\nMinimumQualifications\nBachelor’sdegreeinComputerScience,InformationSystems,Engineering,orarelatedfield,orequivalentexperience.\n\nTypically10+yearsofarchitecture/engineeringexperience,includingsustainedleadershipofenterprise-scale,cross-platformprograms.\n\nExperiencedesigning,governing,anddeliveringcustomer-facinganalytics,reporting,ordata-productplatforms.\n\nHands-onexperiencewithlarge-scaleenterprisedatawarehouseintegrations,dataarchitecture,datamodeling,ELT/ETLpatterns,dataquality,lineage,governance,andprivacy.\n\nExperiencewithSnowflake,Databricks,Spark,SQL,semanticmodels,dataproducts,andanalyticsplatforms.\n\nExperiencewithmodernserviceandAPIdesign,includingREST/JSON,authentication,authorization,versioning,errorhandling,andsecureAPIconsumption.\n\nExperiencedesigninganddeployingGenerativeAIsolutionsinenterpriseenvironments.\n\nDemonstratedexperiencewithRAGarchitectures,includingvectordatabases,embeddings,documentindexing,semanticsearch,retrievalorchestration,andpromptworkflows.\n\nPracticalexperiencewithAgenticAIsolutions,includingmulti-agentsystems,orchestrationframeworks,toolintegration,memorypatterns,reasoningworkflows,andautonomoustaskexecution.\n\nExperiencewithAzureOpenAI,AzureAIFoundry,AzureAISearch,LLMAPIs,embeddingAPIs,vectordatabases,orrelatedAIservices.\n\nStrong understanding of promptengineering,modelevaluation,hallucinationmitigation,guardrails,ResponsibleAIcontrols,andAIapplicationobservability.\n\nAbilitytotranslatecomplexarchitecturedecisionsintoclearrecommendationsfortechnicalandnon-technicalstakeholders.\n\nExperiencealigningproduct,engineering,security,data,andoperationsteamstooperationalizetargetarchitecturesanddelivermeasurablebusinessoutcomes.\n\nCoreCompetencies\nEnterpriseArchitectureLeadership: Setsarchitecturedirection,definesstandards,andguidesteamstowardsecure,scalable,andbusiness-alignedsolutions.\n\nSystemsThinking: Balancescustomerexperience,dataquality,security,scalability,performance,cost,compliance,andoperability.\n\nAIArchitecture&Governance: DesignsresponsibleAIecosystemsthatintegrateenterprisedata,analyticsplatforms,APIs,andintelligentagents.\n\nTechnicalInfluence: Buildsconsensusacrossproduct,engineering,data,security,operations,andexecutivestakeholders.\n\nPragmaticExecution: Movesfromarchitecturestrategytoreferenceimplementations,reusablepatterns,andproduction-readydelivery.\n\nCommunication&Storytelling: Simplifiescomplextrade-offsandclearlycommunicatesrisks,options,andrecommendationstotechnicalandnon-technicalaudiences.\n\nPreferredQualifications\nExperience integrating analytics with BI tools such as Power BI, GoogleLooker,semanticlayers,datacatalogs,andgovernancetooling.\n\nExperiencewithclouddataplatformsandservices,includingSnowflakeonAzure,Databricks,AzureDataFactory,objectstorage,andeventstreamingplatformssuchasKafka.\n\nExperiencewithAzureAIFoundry,AzureOpenAI,AzureAISearch,MicrosoftFabricAIcapabilities,SemanticKernel,LangChain,LangGraph,AutoGen,CrewAI,orsimilarframeworks.\n\nExperienceimplementingvectordatabasesandsemanticretrievalplatformssuchasAzureAISearch,Pinecone,Weaviate,Chroma,orequivalenttechnologies.\n\nExperiencebuildingconversationalanalytics,AIcopilots,knowledgeassistants,andintelligentworkflowautomationsolutions.\n\nExperiencewithAIevaluationframeworks,retrievalqualitymetrics,groundingvalidation,prompttesting,safetyevaluation,andproductionmodelmonitoring.\n\nExperiencedeployingAIapplicationsusingcontainerizedandcloud-nativearchitecturesonAzure.\n\nExperienceinhealthcareIT,regulatedindustries,orotherlarge-scaleenterpriseenvironments.\n\nPhysical Requirements: General Office Demands\nRelocation assistance / allowance is not budgeted for this position\nWe are proud to offer a competitive compensation package at McKesson as part of our Total Rewards. This is determined by several factors, including performance, experience and skills, equity, regular job market evaluations, and geographical markets. The pay range shown below is aligned with McKesson's pay philosophy, and pay will always be compliant with any applicable regulations.In addition to base pay, other compensation, such as an annual bonus or long-term incentive opportunities may be offered. For more information regarding benefits at McKesson, please click here.\nOur Base Pay Range for this position\n$122,100 - $162,800McKesson has become aware of online recruiting-related scams in which individuals who are not affiliated with or authorized by McKesson are using McKesson’s (or affiliated entities, like CoverMyMeds or RxCrossroads) name in fraudulent emails, job postings or social media messages. In light of these scams, please bear the following in mind:\nMcKesson Talent Advisors will never solicit money or credit card information in connection with a McKesson job application.\nMcKesson Talent Advisors do not communicate with candidates via online chatrooms or using email accounts such as Gmail or Hotmail. Note that McKesson does rely on a virtual assistant (Gia) for certain recruiting-related communications with candidates.\nMcKesson job postings are posted on our career site: careers.mckesson.com.\nMcKesson is an Equal Opportunity Employer\nMcKesson provides equal employment opportunities to applicants and employees, without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, age, genetic information, or any other legally protected category. 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