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 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
- Experience and understanding of MongoDB products and capabilities is a plus
- Bachelor's degree in computer science, computer engineering, electrical engineering, systems analysis, or a related field; MS or advanced degree is preferred
Core competencies
- 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
Success Measures
- Data Foundation Delivery (12-18 months)
- Establish golden records for Customer and Product master data domains
- Achieve 95%+ lineage visibility for financial reporting and AI training datasets
- Deploy data governance framework adopted by 3+ business units
- Speed and Access Improvements (12 months)
- Reduce data availability latency from weeks to 5 days or less for 80% of use cases
- Launch semantic layer enabling natural language query for priority business datasets
- Improve data quality scores by 25% for master data domains
- Platform Modernization (18 months)
- Complete build vs. buy decisions for Data Platform, ETL, Data Quality, and Master Data tools
- Implement cross-cloud data integration architecture connecting Data Lake and Warehouse
- Deploy 2+ AI-powered data quality or security monitoring use cases
- Adoption and Value Realization
- Achieve 80%+ stakeholder satisfaction with architecture roadmap clarity
- Enable self-service analytics reducing data teams support tickets by 30%
- Document architecture decisions and patterns with 90%+ team accessibility rating
- Technical Excellence
- Zero critical security or compliance violations in data solutions under this role’s oversight
- Maintain architecture review cadence (bi-weekly) with <5 day SLA on design feedback
About MongoDB
MongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the database for the AI era, enabling innovators to create, transform, and disrupt industries with software. MongoDB’s unified database platform, the most widely available, globally distributed database 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 database and is available across AWS, Google Cloud, and Microsoft Azure.
With offices worldwide and over 60,000 customers, including 75% of the Fortune 100 and AI-native startups, relying on MongoDB for their most important applications, we’re powering the next era of software.
Our compass at MongoDB is our Leadership Commitment, guiding how and why we make decisions, show up for each other, and win. It’s what makes us MongoDB.
To drive the personal growth and business impact of our employees, we’re committed to developing a supportive and enriching culture for everyone. From employee affinity groups, to fertility assistance and a generous parental leave policy, we value our employees’ wellbeing and want to support them along every step of their professional and personal journeys. Learn more about what it’s like to work at MongoDB, and help us make an impact on the world!
MongoDB is committed to providing any necessary accommodations for individuals with disabilities within our application and interview process. To request an accommodation due to a disability, please inform your recruiter.
MongoDB, Inc. provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type and makes all hiring decisions without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.
REQ ID: 1273368200