Senior Staff Enterprise Architect, Data

About the Role We 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 Responsibilities - Data Strategy & Roadmap - Design semantic layer architecture standardizing business metrics enterprise-wide. Define governance guardrails ensuring natural language queries access validated master data sources - Develop 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 governance - Define 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 cases - Develop 12-24 month data architecture roadmaps for Finance, Sales, Product, and People. Identify capability gaps and recommend technology investments with business value and effort estimates - Systems Design & Solution Leadership - Evaluate AI-powered data observability platforms for quality monitoring, pipeline failure prediction, and data classification. Define requirements, lead vendor POCs, and establish integration patterns - Define 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 implementation - Establish 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 board - Design 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 implementation - Apply 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 leaders - Lead 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 integrations - Technical Execution & Delivery - Participate 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 debt - Serve 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 roadmap - Governance, Standards & Risk Management - Define 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 implementation - Collaborate 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 artifacts - Establish 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 standards - Conduct impact assessments for major initiatives analyzing data flows, dependencies, performance, and cost. Present design alternatives with risk/be