Senior Staff Enterprise Architect, Data

Навыки

• AWS
• Azure
• BI-инструменты
• Data Governance
• Data Lake
• Data Quality
• DWH

О компании и продукте

• 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.
• MongoDB is built for change, empowering our customers and our people to innovate at the speed of the market. We have redefined the data platform for the AI era, enabling builders to create, transform, and disrupt industries with software. MongoDB’s unified data platform, the most widely available, globally distributed data platform 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 data platform and is available across AWS, Google Cloud, and Microsoft Azure.

Задачи

• 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
• Partner with Product Management on feasibility, MVP scoping, and scaling plans
• Establish regular touchpoints with Data Engineering, Enterprise Architecture, and business leaders

Требования

• 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)

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