other
Posted 5 days agoSenior Staff Enterprise Architect, Data
at MongoDB
Hybrid
Responsibilities
- 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.
- 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
- Establish regular touchpoints with Data Engineering, Enterprise Architecture, and business leaders
- Lead solution scoping workshops, provide effort estimates, and identify dependencies.
- Conduct quarterly audits to assess adherence and identify technical debt
- 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.
- 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.
- Create and maintain architecture documentation: data flow diagrams, master data models, integration patterns, and technology stack references.
- 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.
- Mentor 5-10 data engineers and architects through quarterly career discussions, design reviews, and problem-solving.
- Establish golden records for Customer and Product master data domains
- Deploy data governance framework adopted by 3+ business units
- 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
- 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
- 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
- Maintain architecture review cadence (bi-weekly) with <5 day SLA on design feedback About MongoDB
Requirements
- 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.
- 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.
- Serve as early adopter of MongoDB Atlas and Voyage AI (including vector search for RAG).
- Provide governance oversight for AI/ML initiatives ensuring training data meets quality and lineage standards
- Target: 80% awareness of data governance basics within 12 months
- 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
- 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
- Methodologies: Working knowledge of Agile, ITIL, and design thinking practices Success Measures
- Achieve 95%+ lineage visibility for financial reporting and AI training datasets
- 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.
- With offices worldwide and over 67,000 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.
- Learn more about what it’s like to work at MongoDB , and help us make an impact on the world!
Experience
- 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
Benefits
- 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.
- MongoDB’s base salary range for this role is posted below.
- Compensation at the time of offer is unique to each candidate and based on a variety of factors such as skill set, experience, qualifications, and work location.
- Salary is one part of MongoDB’s total compensation and benefits package. Other
- benefits for eligible employees may include: equity, participation in the employee stock purchase program, flexible paid time off, 20 weeks fully-paid gender-neutral parental leave, fertility and adoption assistance, 401(k) plan, mental health counseling, access to transgender-inclusive health insurance coverage, and health
- benefits offerings. Please note, the base salary range listed below and the
- MongoDB’s base salary range for this role in the U.S. is: $177,000 — $349,000 USD
Additional details
- We are seeking a Staff Enterprise Architect, Data to lead the strategy, design, and modernization of our enterprise data landscape.
- Over the next 12-18 months, you will enable self-service data access and natural language query capabilities for business users.
- We're looking to speak with candidates based in the San Francisco Bay Area for our hybrid working model. Key Responsibilities
- Apply product thinking to data platforms, treating internal consumers as customers.
- Partner with Product Management on feasibility, MVP scoping, and scaling plans.
- 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.
- Evaluate MongoDB objectively in build/buy decisions, documenting capability gaps.
- Share enterprise feedback to influence product roadmap