Senior Staff Enterprise Architect, Data (Palo Alto)

About the RoleWe are seeking a Staff Enterprise Architect, Data to lead the strategy, design, and modernization of our enterprise data landscape. This role operates at the intersection of data architecture, engineering, and AI enablement, defining solutions to integrate our Data Lake and Data Warehouse across multi-cloud platforms.Over the next 12-18 months, you will enable self-service data access and natural language query capabilities for business users. You will architect Master Data Management and data lineage frameworks ensuring AI models operate on high-quality, governed data. You will also evaluate and implement AI-powered tools to automate data quality monitoring and enhance data security.We're looking to speak with candidates based in the San Francisco Bay Area for our hybrid working model.Key ResponsibilitiesData Strategy & RoadmapDesign semantic layer architecture standardizing business metrics enterprise-wide. Define governance guardrails ensuring natural language queries access validated master data sourcesDevelop Master Data strategy for Customer and Product domains (phases 1-2), Finance and People to follow. Define golden record requirements, stewardship models, and system-of-record hierarchy. Partner with business owners on master data governanceDefine cross-cloud data integration strategy and reference architecture. Specify patterns (federation, replication, abstraction layer) balancing performance, cost, and data freshness. Document trade-offs and recommend implementations for batch and near-real-time use casesDevelop 12-24 month data architecture roadmaps for Finance, Sales, Product, and People. Identify capability gaps and recommend technology investments with business value and effort estimatesSystems Design & Solution LeadershipEvaluate AI-powered data observability platforms for quality monitoring, pipeline failure prediction, and data classification. Define requirements, lead vendor POCs, and establish integration patternsDefine data ingestion architecture reducing availability from weeks to 3-5 days (batch) and under 15 minutes (real-time). Specify ELT patterns using CDC where feasible. Document source system constraints and partner with engineering on phased implementationEstablish build vs. buy frameworks for Data Platform, ETL, Data Quality, and Master Data tooling. Define POC criteria and scoring models. Oversee POC execution and present recommendations with TCO analysis to the architecture review boardDesign data solutions for priority initiatives (customer 360, financial reporting, AI pipelines). Ensure designs address quality SLAs, monitoring, security controls, and operational documentation. Validate through architecture review before implementationApply product thinking to data platforms, treating internal consumers as customers. Partner with Product Management on feasibility, MVP scoping, and scaling plans. Establish regular touchpoints with Data Engineering, Enterprise Architecture, and business leadersLead solution scoping workshops, provide effort estimates, and identify dependencies. Serve as escalation for complex design questions on cross-system flows, high-volume schema design, and vendor integrationsTechnical Execution & DeliveryParticipate in design reviews and checkpoints to validate alignment with architectural standards. Provide course-correction when needed, balancing consistency with pragmatic tradeoffs. Conduct quarterly audits to assess adherence and identify technical debtServe as early adopter of MongoDB Atlas and Voyage AI (including vector search for RAG). Evaluate MongoDB objectively in build/buy decisions, documenting capability gaps. Share enterprise feedback to influence product roadmapGovernance, Standards & Risk ManagementDefine data lineage strategy and technical requirements. Establish coverage targets: 100% for financial/AI data within 12 months, 80% for operational dashboards within 18 months. Map lineage to regulatory requirements (SOX, GDPR)Design automated data quality frameworks with validation rules, anomaly detection, and quarantine workflows. Define quality metrics and SLAs by domain Specify check integration points and alerting processes. Partner with Data Operations on implementationCollaborate with InfoSec on data access governance and security monitoring tools. Define anomalous access patterns, data classification schema, and security-lineage integration requirements. Document policies and controls in architecture artifactsEstablish data architecture principles and design patterns. Chair bi-weekly architecture review board meetings. Maintain ADRs documenting key decisions. Provide governance oversight for AI/ML initiatives ensuring training data meets quality and lineage standardsConduct impact assessments for major initiatives analyzing data flows, dependencies, performance, and cost. Present design alternatives with risk/benefit analysis highlighting security, privacy, and technical debt. Establish mitigation plans before approvalCreate and maintain architecture documentation: data flow diagrams, master data models, integration patterns, and technology stack references. Update quarterly or as needed. Ensure accessibility for engineering teamsTeam Leadership & EvangelismBuild architecture community of practice: host monthly deep-dives, share best practices, facilitate cross-team collaboration, and maintain a knowledge baseDevelop data literacy enablement: quarterly workshops, office hours, and documentation. Translate technical concepts into business impact. Target: 80% awareness of data governance basics within 12 monthsMentor 5-10 data engineers and architects through quarterly career discussions, design reviews, and problem-solving. Focus on systems thinking, stakeholder communication, and balancing idealism with pragmatismQualifications12+ years in IT with 7+ years in Data Architecture, Data Engineering, or Enterprise Architecture roles10+ years across three or more: data architecture, data engineering, database management, analytics, or cloud infrastructureProven ability to architect solutions that bridge Data Lakes and Warehouses in separate clouds (e.g., AWS, Azure, Google Cloud)Hands-on experience with Master Data and data lineage tools. Must have designed master data models for at least two domains: Customer, Product, Finance, or PeopleExperience evaluating or implementing AI/ML tools for data quality monitoring and automated data classificationProven success reducing data latency using CDC, streaming, or real-time integration patterns.Proficient in SQL and Python. Experience with modern data platforms (Snowflake, Databricks, BigQuery, or similar). RAG architectures and vector databases are a plusLed architecture for large-scale implementations: CRM, Enterprise Data Platforms, Data Lakes, or ERP systemsExperience managing vendor evaluations, contract negotiations, and ongoing partner relationshipsExperience and understanding of MongoDB products and capabilities is a plusBachelor's degree in computer science, computer engineering, electrical engineering, systems analysis, or a related field; MS or advanced degree is preferredCore competenciesLeadership: Leads cross-functional teams through influence, navigates conflict, and drives results without direct authorityCommunication: Translates complex technical concepts for executive and business audiences. Strong written, visual, and presentation skillsFinancial & Analytical: Builds business cases with TCO analysis and ROI projections. Defines measurable success metricsChange Management: Drives technology adoption while addressing stakeholder concerns and resistanceTechnical Pragmatism: Cuts through vendor hype. Makes build vs. buy decisions grounded in business value and riskInfluence Without Authority: Shapes technical direction through credibility and collaboration, not mandateMethodologies: Working knowledge of Agile, ITIL, and design thinking practicesSuccess MeasuresData Foundation Delivery (12-18 months)Establish golden records for Customer and Product master data domainsAchieve 95%+ lineage visibility for financial reporting and AI training datasetsDeploy data governance framework adopted by 3+ business unitsSpeed and Access Improvements (12 months)Reduce data availability latency from weeks to 5 days or less for 80% of use casesLaunch semantic layer enabling natural language query for priority business datasetsImprove data quality scores by 25% for master data domainsPlatform Modernization (18 months)Complete build vs. buy decisions for Data Platform, ETL, Data Quality, and Master Data toolsImplement cross-cloud data integration architecture connecting Data Lake and WarehouseDeploy 2+ AI-powered data quality or security monitoring use casesAdoption and Value RealizationAchieve 80%+ stakeholder satisfaction with architecture roadmap clarityEnable self-service analytics reducing data teams support tickets by 30%Document architecture decisions and patterns with 90%+ team accessibility ratingTechnical ExcellenceZero critical security or compliance violations in data solutions under this role’s oversightMaintain architecture review cadence (bi-weekly) with <5 day SLA on design feedbackAbout MongoDBMongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the data platform for the AI era, enabling builders to create, transform, and disrupt industries with software. MongoDB’s unified data platform, the most widely available, globally distributed data platform on the market, helps organizations modernize legacy workloads, embrace innovation, and unleash AI. Our cloud-native platform, MongoDB Atlas, is the only globally distributed, multi-cloud data platform and is available across AWS, Google Cloud, and Microsoft Azure.With offices worldwide and over 67,000 customers, including 75% of the Fortune 100 and AI-native startups, relying on MongoDB for their most important appl