Backend & Cloud Engineering
PostgreSQL & MongoDB Development
Database work done properly: queries, indexes, backups and recovery.
- Typical timeline
- 1-4 weeks for an audit and remediation; ongoing support available
- Category
- Backend & Cloud Engineering
- Engagement
- Fixed-scope phases
- You receive
- Code, data & documentation
Overview
Beyond initial design, databases need ongoing engineering: queries written efficiently, indexes maintained, backups verified by actual restores, and replication configured before you need it. This is the work that quietly keeps systems running.
The database works until the day it does not
Backups are configured but have never been restored. Replication was deferred. Nobody monitors connection counts or slow queries. Then a disk fills, a query locks a table during business hours, and it becomes clear how much depended on something nobody owned.
You will recognise this if
- Nobody has ever tested restoring from a backup
- There is no replica, so any failure means complete downtime
- Slow queries are discovered through user complaints
- Database credentials are shared and have never been rotated
What you get out of it
The difference this makes
Backups you have actually tested
Automated backups with periodic verified restores and a documented recovery procedure with a known recovery time.
Failure without disaster
Replication and failover mean a server loss is a short interruption rather than an extended outage with data loss.
Problems found before users find them
Slow query logging and monitoring surface degradation early, while it is still a small fix.
Efficient queries
Reports that took minutes typically come down to seconds once the query and indexes are addressed properly.
What we build
- Complex query development: CTEs, window functions, aggregations and full-text search
- Stored procedures, functions and triggers where they genuinely belong in the database
- Index design, maintenance and bloat management
- Streaming replication with read replicas and failover configuration
- Automated backups with point-in-time recovery and tested restore drills
- Connection pooling with PgBouncer and resource configuration tuning
- Monitoring: slow queries, locks, connection counts, disk and replication lag
- MongoDB aggregation pipelines, sharding and replica sets
- pgvector setup for embedding storage and similarity search
- Role and privilege design on least-privilege principles
How it gets used
A growing application outpacing its database
Query tuning, index correction, connection pooling and a read replica for reporting, restoring performance without a rewrite.
A business with no recovery plan
Automated backups, verified restore testing and a documented recovery procedure with a stated recovery time objective.
An AI application needing vector search
pgvector configured with appropriate index types for similarity search at the scale the application actually needs.
Technology we typically use
You do not need to care about this list — but your next developer will.
- PostgreSQL 16+
- MongoDB
- pgvector
- PgBouncer
- pgBackRest / WAL archiving
- Prometheus monitoring
Timeline: Typically 1-4 weeks for an audit and remediation; ongoing support available
Questions
PostgreSQL & MongoDB Development — common questions
Something else on your mind? Ask us directly — it is usually faster than reading.
Our reports are very slow. Is more server capacity the answer?
How often should backups run, and where should they be stored?
Can you manage our database on an ongoing basis?
Related services
Businesses that need this often need one of these as well.
Thinking about postgresql & mongodb development?
Tell us about your situation. We will tell you whether this is the right service, what it would involve, and roughly what it would cost.