Senior Staff Enterprise Architect, Data

MongoDB•Palo Alto, CA

18h•Hybrid

About The Position

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.

Requirements

12+ years in IT with 7+ years in Data Architecture, Data Engineering, or Enterprise Architecture roles
10+ years across three or more: data architecture, data engineering, database management, analytics, or cloud infrastructure
Proven 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 People
Experience evaluating or implementing AI/ML tools for data quality monitoring and automated data classification
Proven 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 plus
Led architecture for large-scale implementations: CRM, Enterprise Data Platforms, Data Lakes, or ERP systems
Experience managing vendor evaluations, contract negotiations, and ongoing partner relationships
Bachelor's degree in computer science, computer engineering, electrical engineering, systems analysis, or a related field; MS or advanced degree is preferred
Leadership: Leads cross-functional teams through influence, navigates conflict, and drives results without direct authority
Communication: Translates complex technical concepts for executive and business audiences. Strong written, visual, and presentation skills
Financial & Analytical: Builds business cases with TCO analysis and ROI projections. Defines measurable success metrics
Change Management: Drives technology adoption while addressing stakeholder concerns and resistance
Technical Pragmatism: Cuts through vendor hype. Makes build vs. buy decisions grounded in business value and risk
Influence Without Authority: Shapes technical direction through credibility and collaboration, not mandate
Methodologies: Working knowledge of Agile, ITIL, and design thinking practices

Nice To Haves

Experience and understanding of MongoDB products and capabilities is a plus

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