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Staff Engineer – Data Federation and Online Archive
| Company | MongoDB |
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| Location | Seattle, WA, USA |
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| Salary | $137000 – $270000 |
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| Type | Full-Time |
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| Degrees | |
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| Experience Level | Expert or higher |
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Requirements
- 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.
Responsibilities
- 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
- 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
- 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.
Preferred Qualifications
- Proficiency in Go or Java (preferred, but not required)
