MongoDB Performance & Query Optimization Consultant

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We are a growing platform. We’re currently on MongoDB Atlas v6.0.23 and are seeking an expert to help us optimize query performance, index strategy, and schema design. Our system is showing key warning signs of inefficiency and under-optimized structure, and we want to address these now before scaling further. Current Database Issues & Findings: Performance Red Flags: Query Targeting Alert: The ratio of scanned-to-returned documents exceeds 1000, indicating missing or misaligned indexes. Excessive $lookup Usage: Over 50% of our slow operations involve $lookup. More than 100 $lookup queries have been detected. These joins are dragging performance and likely point to a schema that needs normalization or embedding. Unused Indexes Detected: MongoDB Atlas has flagged location.coordinates_2dsphere and style_references_1 as unused for 9+ days. We’re unsure whether to migrate these to production or eliminate them. Inefficient Operation Load Even at Low Volume: With just 5.15 ops/sec, we’re already seeing: 4.49 total hours of latency 36.39 ms average query latency 52.71 ms P95 latency These numbers reveal that our system is not scaling efficiently. Even light traffic generates hours of cumulative delay. This confirms the system will struggle under load and adds urgency to this optimization. Lack of Profiling & Monitoring: We are not yet leveraging MongoDB’s profiler, query plans, or other native tools effectively. We need guidance implementing and interpreting them. What You’ll Do: Audit all $lookup usage and advise whether to embed, pre-aggregate, or optimize with indexes Refactor or advise on reducing unnecessary joins using MongoDB’s flexible document model Resolve the query targeting alert by aligning indexes with actual access patterns Evaluate current and unused indexes; advise which should be kept, dropped, or ported to production Use Atlas Profiler and query plans to identify additional latency drivers Guide schema redesign or restructuring to improve read/write performance Document findings and create a go-forward strategy we can maintain internally Requirements: Deep experience with MongoDB (ideally MongoDB Atlas) and schema design best practices Mastery of $lookup, compound indexing, sparse/TTL indexes, and performance tuning Experience interpreting Atlas Profiler, Metrics, and Query Insights Ability to communicate complex backend concepts clearly to a non-technical founder Bonus: Familiarity with modern JavaScript/TypeScript stacks (Node.js, Next.js) Share what you think your findings and optimizations could increase our performance and speed by. Thank you

Keyword: Software Development

Query Optimization MongoDB Performance Optimization