A relational schema in documents
Every entity in its own collection, joined back together with $lookup on every request — all the cost of joins without the planner that makes them cheap.
[ Development ]
MongoDB Development & Consulting
Schemaless doesn't mean design-less.
MongoDB is quick to start with because it lets you skip the schema. Two years later that shows up as slow aggregations, sixteen-megabyte documents and queries that scan every collection. I design the document model around how your application actually reads data, and fix the ones already in production.
Explain plans, not guesses · Tested on a copy first · Written findings
Modeledaround real queries
explain()before any change
Atlasor self-hosted
You ownthe code and data
[ Why teams call ]
Where MongoDB projects go wrong.
MongoDB is a good database. Most of the pain comes from a document model that made sense for the first version and nothing since.
Documents that never stop growing
Unbounded arrays of comments, events or line items embedded in one document, heading toward the 16 MB limit and rewritten in full on every update.
Collection scans in production
Queries and aggregation pipelines with no supporting index, fine in development, reading millions of documents per request in production.
Exposed or unauthenticated instances
A self-hosted MongoDB bound to 0.0.0.0 with auth disabled, which is exactly what automated ransom scanners look for.
No tested backups
A replica set treated as a backup, which it is not — a bad delete replicates to every member within milliseconds.
Old versions and drivers
MongoDB 4.x or early 5.0 out of support, drivers years behind, and an upgrade that has to step through each major version in turn.
[ How it works ]
Review, fix, hand over.
01 — Days 1–3
Review
The profiler, slow query log, index usage stats and a sample of real documents. You get a written list of what is slow, what is risky and what I would change first, with an estimate for each.
02 — Week 1+
Fix or build
Indexes built in the background, pipelines rewritten, documents reshaped with scripted migrations — all rehearsed on a copy, then applied in reviewable steps.
03 — Handover
Document
Before-and-after explain output, the data model written down with the reasoning behind it, and a backup and restore procedure that has actually been tested.
[ What actually changes ]
What a MongoDB engagement covers.
New builds, rescues of existing apps and one-off reviews. Pick the part you need.
Data model & performance
- Document modeling — embed or reference decided per relationship, based on how data is read and how fast it grows.
- Indexes — compound indexes that follow the equality, sort, range rule, plus partial, TTL and text or Atlas Search indexes where they fit.
- Aggregation pipelines — $match and $project early, $lookup kept to a minimum, and pipelines that stay within memory limits.
- Schema validation — JSON Schema validators on collections, so bad data is rejected at the database rather than discovered later.
- Node.js and Mongoose apps — lean queries, sensible population, connection reuse in serverless functions and TypeScript types that match the data.
Operations & migration
- Atlas or self-hosted — an honest comparison of cost and effort for your size, and setup or migration either way.
- Replica sets and sharding — replica sets for availability, and sharding only when the data genuinely needs it, with a shard key chosen carefully.
- Security — authentication, role-based access, network restrictions, TLS and encryption at rest, checked against how the instance is actually exposed.
- Backups — Atlas backups or mongodump and snapshot schedules, with point-in-time restore tested and timed.
- Version upgrades — stepped through each major version with driver and feature-compatibility checks at every stage.
- MongoDB ↔ SQL migrations — into PostgreSQL when the data turned out relational, or out of SQL when documents really fit better.
[ Example engagement ]
Aggregation-heavy API brought back under control.
A Node.js and Mongoose API where one report pipeline scanned the full events collection. Reshaped the pipeline, added two compound indexes and bucketed the unbounded arrays.
Illustrative example of a typical engagement. Figures vary with the state of your systems and are not a guarantee of a specific outcome.
2compound indexes added
88%fewer documents examined
71%lower p95 on the report
1 weekreview to handover
[ Free · no obligation ]
MongoDB slow or growing out of control?
Describe the symptom and share an explain() output if you have one. You get an honest assessment of the cause and a fixed price to fix it properly.
[ Pricing ]
Straightforward pricing. $20/hour or fixed.
Tell me the number you have to work with. I'll tell you honestly what's achievable within it — and if it isn't enough, I'll say so before we start rather than halfway through.
Hourly rate $20/hoursmall jobs and ongoing work · fixed price for defined projects
Fixed project price
Scope agreed in writing, price agreed in writing, before any work starts. No hourly creep and no invoice you haven't already approved.
Monthly retainer
For ongoing work — maintenance, monitoring, updates and small changes. Month to month, cancel whenever, no minimum term.
$20/hour for small jobs
For a single bug, a short task or ongoing tweaks, where writing a full scope would cost more than simply doing the work. Billed in real time, reported in writing.
Budget too tight for the whole thing? I'll often suggest doing the highest-value part first and the rest later, rather than doing all of it badly.
[ Questions ]
Common questions.
What does it cost to hire a MongoDB developer?
Small or open-ended jobs — reviewing a slow query, checking security, answering a modeling question — are billed hourly at $20 per hour. Defined projects such as a migration, an upgrade or a new build get a fixed price agreed in writing before I start.
Should we be using MongoDB at all?
Often, yes. MongoDB suits data that is naturally document-shaped, varies in structure or is read as a whole. If your data is heavily relational and you are writing $lookup everywhere, PostgreSQL may serve you better, and I will say so rather than keep patching.
Atlas or self-hosted?
For most teams, Atlas. Backups, upgrades, monitoring and security defaults are handled for you, and the cost usually compares well with the engineering time self-hosting takes. Self-hosting makes sense at larger scale or with specific compliance or data-residency needs.
Do we need sharding?
Probably not yet. A well-indexed replica set on sensible hardware handles far more than most applications need. Sharding adds real operational complexity, and a poor shard key is very hard to undo, so I only recommend it when the numbers call for it.
Can you secure an exposed MongoDB instance?
Yes, and quickly. Authentication on, network access restricted, TLS enabled, users given only the roles they need — and a check of the logs for signs the instance was already accessed.
Can you migrate us between MongoDB and SQL?
Yes, in either direction. Moving to PostgreSQL means designing a relational schema from the documents, handling inconsistent fields and changing application queries. I run both side by side on a copy before cutover, so differences surface before your users see them.
Do you work on Mongoose and Node.js code too?
Yes. Most MongoDB performance problems are fixed partly in the application — how queries are built, what gets populated, how connections are reused. I work in your repository and submit normal pull requests.
Where are you based?
I work remotely with teams in the US, UK, Europe, Canada and Australia. Most work is asynchronous, and I overlap with your hours for calls and for any change that touches production.
[ Related services ]
Related data & backend work.
PostgreSQL ConsultingSlow queries, indexing, upgrades and migrations to Postgres.GraphQL API DevelopmentGraphQL schemas and servers that stay fast and secure.Node.js DevelopmentAPIs, services and background workers that stay up.Python & FastAPITyped, async Python APIs and data services.Docker & ContainersContainerise an existing app so environments finally match.AWS Server SetupProduction-ready VPC, compute, database and CI/CD from scratch.
[ Let's talk ]
Tell me about your data.
Describe what you're building or what's going wrong. You'll get a straight answer on scope, cost and timeline — from the person who would actually do the work.