Senior Staff Enterprise Architect, Data

Role details

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/benefit analysis highlighting security, privacy, and technical debt. Establish mitigation plans before approval Create 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 teams Team Leadership & Evangelism Build architecture community of practice: host monthly deep-dives, share best practices, facilitate cross-team collaboration, and maintain a knowledge base Develop data literacy enablement: quarterly workshops, office hours, and documentation. Translate technical concepts into business impact. Target: 80% awareness of data governance basics within 12 months Mentor 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 pragmatism Qualifications 12+ years in IT with 7+ years in Data Architecture, Data Engineering, or Enterprise Architecture roles 10+ years across three or more: data ar