Senior Staff Enterprise Architect, MongoDB
Data
$177-349k
SQL
MongoDB
AWS
GCP
Python
Azure
Snowflake
BigQuery
Azure Databricks
Expert level
San Francisco Bay Area
Office located in Palo Alto, CA
Developer data platform
Open for applications
Developer data platform
1001+ employees
B2BEnterpriseBig dataCloud Computing
Open for applications
$177-349k
SQL
MongoDB
AWS
GCP
Python
Azure
Snowflake
BigQuery
Azure Databricks
Expert level
San Francisco Bay Area
Office located in Palo Alto, CA
1001+ employees
B2BEnterpriseBig dataCloud Computing
Company mission
To empower innovators to create, transform, and disrupt industries by unleashing the power of software and data.
Role
Who you are
- 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
- 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
What the job involves
- 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
- 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
- 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
- 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
- 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
- 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
- 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
Our take
MongoDB is an open-source, cross-platform, document-oriented database system. It stores data as JSON-like documents and is written in C++, Go, JavaScript and Python.
Essentially, the company develops tools and blueprints to help businesses and organisations modernise their legacy applications, migrating them to the MongoDB database and the MongoDB Atlas cloud database. With this initiative, MongoDB is particularly taking aim at Oracle customers with ageing applications running on the Oracle relational database system.
Since its release, MongoDB has become one of the most popularly used NoSQL database systems due to its ease of use and efficiency. It is also the fastest-growing database ecosystem, and boasts hundreds of millions of downloads. Recently, the company announced a partnership with Patronus AI, an automated evaluation and security platform, through which it will bring automated LLM evaluation and testing capabilities to enterprise customers.
Freddie
Company Specialist at Welcome to the Jungle
Insights
Top investors
13% employee growth in 12 months
Company
Funding (last 2 of 8 rounds)
Jan 2015
$80m
SERIES G
Oct 2013
$150m
SERIES F
Total funding: $311.1m
Company benefits
- Rich health insurance coverage
- Virtual & on-site fitness classes
- Health screenings & telemedicine
- Access to transgender-inclusive health insurance coverage
- Global and internal mobility opportunities
- Equity & Employee Stock Purchase Program
- Pension & retirement programs
- Income Protection
- Flexible PTO is offered to every US employee & competitive time off policies for non-US employees
- Employee Assistance Program
- Mental health counseling
- Free meditation app access
- Fertility & adoption financial assistance
- Parental counseling for new parents
- 20 weeks of fully paid gender neutral parental leave & flexible work arrangements
- 4 weeks of emergency care leave
Company HQ
Theater District, New York, NY
Leadership
Dwight Merriman
(Co-Founder)Previously CTO and Co-founder at DoubleClick for 10 years, and Chairman at AlleyCorp for 15 years.
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