Senior Staff Enterprise Architect, Data

MongoDB MongoDB · Enterprise · Palo Alto, CA · Enterprise Architecture

Seeking a Staff Enterprise Architect, Data to lead the strategy, design, and modernization of the enterprise data landscape, focusing on integrating Data Lake and Data Warehouse across multi-cloud platforms. The role will enable self-service data access, natural language query, architect MDM and data lineage for AI models, and evaluate AI tools for data quality and security. Experience with RAG architectures and vector databases is a plus.

What you'd actually do

  1. Data Strategy & Roadmap
  2. Systems Design & Solution Leadership
  3. Technical Execution & Delivery
  4. Governance, Standards & Risk Management
  5. Team Leadership & Evangelism

Skills

Required

  • 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
  • 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)
  • 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

Nice to have

  • Experience and understanding of MongoDB products and capabilities is a plus
  • RAG architectures and vector databases are a plus
  • MS or advanced degree is preferred

What the JD emphasized

  • 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

Other signals

  • enabling self-service data access and natural language query capabilities for business users
  • architecting Master Data Management and data lineage frameworks ensuring AI models operate on high-quality, governed data
  • evaluating and implementing AI-powered tools to automate data quality monitoring and enhance data security