Staff Engineer, Data Federation and Online Archive

MongoDB’s mission is to empower innovators to create, transform, and disrupt industries by unleashing the power of software and data. We enable organizations of all sizes to easily build, scale, and run modern applications by helping them modernize legacy workloads, embrace innovation, and unleash AI. Our industry-leading developer data platform, MongoDB Atlas, is the only globally distributed, multi-cloud database and is available in more than 115 regions across AWS, Google Cloud, and Microsoft Azure. Atlas allows customers to build and run applications anywhere—on premises, or across cloud providers. With offices worldwide and over 175,000 new developers signing up to use MongoDB every month, it’s no wonder that leading organizations, like Samsung and Toyota, trust MongoDB to build next-generation, AI-powered applications.

About the Team

MongoDB Atlas Data Federation enables customers to query, transform, and analyze data across multiple sources (MongoDB clusters, cloud object storage, and external databases) through a unified MongoDB query interface—without moving or copying the underlying data. Our system processes hundreds of millions of queries per month and handles exabytes of customer data at scale.

MongoDB Atlas Online Archive provides low-cost, tiered storage for managing infrequently-accessed, read-only data. By optimizing storage layouts during ingestion and rebalancing data dynamically, Online Archive ensures efficient query performance and scalability while managing petabytes of customer data in a rapidly growing system.

About the Role

As a Staff Engineer on the Atlas Data Federation and Archiving team, you will lead the design, optimization, and scalability of our storage and federated query systems. This role focuses on high-performance distributed storage, data lifecycle management, and efficient data retrieval at scale.

You will work on storage optimization, query execution, and cost-effective data retention strategies—ensuring reliability, performance, and efficiency for thousands of MongoDB Atlas customers who depend on our solutions for critical business operations.

This is a high-impact role for engineers passionate about large-scale data storage, distributed query processing, and system resilience.

This role can be based in New York City, Austin, San Francisco, Seattle, or remotely in the United States.

What You’ll Do

Storage & Data Processing Performance

• Architect and optimize large-scale storage solutions for federated data access, ensuring efficient retrieval, indexing, and query performance
• Optimize data archival pipelines for high-throughput ingestion, durability, and cost-efficiency
• Improve data tiering and lifecycle policies for moving and querying data efficiently across hot, warm, and cold storage tiers
• Reduce operational costs through intelligent storage layout, compaction strategies, and query execution optimizations

Distributed Query & Execution Engine

• Improve and scale our distributed query execution engine, optimizing it for multi-source federated queries and data lake processing
• Enhance query performance across object storage (e.g., S3, GCS, Azure Blob) by optimizing indexing, partitioning, and compaction techniques
• Implement workload-aware autoscaling for query execution and data processing
• Reduce incident rates by improving system resilience, failover mechanisms, and observability

Technical Leadership & Mentorship

• Guide architectural decisions and lead design reviews across engineering teams
• Mentor engineers in distributed systems, data storage optimization, and operational excellence
• Partner with Product Management to define the technical roadmap for storage and data federation solutions
• Participate in on-call rotation, providing senior oversight for incident response and postmortem retrospectives

What We Look For

• 10+ years experience in software engineering, with a focus on backend and distributed storage systems
• Expertise in large-scale storage systems, such as distributed databases, cloud object storage (S3, Azure Blob, GCS), or data lake technologies (Iceberg, Delta Lake, Hudi, etc.)
• Strong background in designing and optimizing storage layers, indexing, and data lifecycle management
• Experience optimizing query engines for high-volume, low-latency federated data access
• Track record of improving system reliability, observability, and cost-efficiency
• Experience with Kubernetes-based deployment of distributed storage or query systems
• Proficiency in Go or Java (preferred, but not required)
• Deep understanding of query optimizers, storage formats (Parquet, ORC), and indexing strategies
• Experience with disaggregated storage and cloud-native data lake solutions
• Proven ability to lead technical initiatives as an individual contributor while mentoring senior engineers and driving technical excellence within a team.

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: 1263076997