Hire Database Developers — PostgreSQL, MongoDB & More

Application developers know how to write queries, but database specialists know how the query planner works, how indexes are physically structured, how to design schemas that age well, and how to diagnose performance problems that appear only under production traffic patterns. A single week of database expert time often produces performance improvements that application developers would spend months trying to achieve by trial and error.

Standard placement is 48 hours from contract signing. Database engineering is a specialised field. For engineers with specific expertise — Oracle, Cassandra, time-series databases like TimescaleDB — one week is more realistic as we source the right specialist from our bench.

PostgreSQL is our primary specialism — it is the most capable open-source relational database and our default recommendation for most projects. We also have deep expertise in MongoDB, Redis, Elasticsearch, and MySQL/MariaDB. For specialised use cases, we have engineers with TimescaleDB, ClickHouse, and DynamoDB experience.

We start with the slow query log — identifying the queries that consume the most cumulative time under real load, not just the single slowest query. For PostgreSQL, we use EXPLAIN (ANALYSE, BUFFERS) to see the actual execution plan, look for sequential scans on large tables, nested loop mismatches on high-row-count joins, and excessive buffer hits indicating cache pressure. pg_stat_statements gives us aggregate statistics across thousands of executions.

Yes. Database migrations between engines are complex but manageable with the right approach. We assess SQL compatibility differences, identify functions and syntax that require rewriting, migrate data with pgloader or custom ETL scripts, validate row counts and data integrity, and plan the cutover to minimise downtime. The application layer is updated alongside the database migration.

Zero-downtime schema migrations require the expand-contract pattern: first add the new column or table while the old structure still works, deploy application code that writes to both structures, migrate data in the background, then deploy code that reads from only the new structure, and finally drop the old column in a cleanup release. This approach takes longer but avoids locking tables and allows instant rollback at every step.

PostgreSQL creates a process for each connection, which is expensive. Applications with many concurrent workers quickly exhaust connection limits and degrade database performance. PgBouncer sits in front of PostgreSQL and maintains a pool of real database connections, multiplexing many application connections onto a smaller number of actual connections. Properly configured, it dramatically increases the number of concurrent application instances a single database can serve.

Yes. MongoDB schema design is fundamentally different from relational design — embedding related data for read performance versus referencing for write flexibility, designing for query patterns rather than normalisation rules, and understanding the impact of document size on memory and storage. Our developers have designed MongoDB schemas for high-write-volume applications and know when MongoDB is the right tool and when it is not.

We choose Redis data structures based on the access pattern: strings for simple key-value cache, sorted sets for leaderboards and rate limiting, hashes for session storage, and streams for event sourcing. Cache invalidation strategy is designed upfront — TTL-based expiry for data that changes on a schedule, event-driven invalidation for data that changes on writes. We instrument cache hit rate and monitor for cache stampede conditions.

Yes. For simple full-text search on small to medium datasets, PostgreSQL's built-in tsvector/tsquery full-text search with GIN indexes is often sufficient. For complex faceted search, relevance ranking, and large document volumes, Elasticsearch or OpenSearch is the right tool. We implement the search layer, keep data in sync with the primary database via change data capture or application events, and tune relevance scoring for your specific content.

We configure automated continuous WAL archiving for PostgreSQL (enabling point-in-time recovery to any second within the retention window), daily full backups, and periodic restoration tests to verify the backup process actually works. Recovery procedures are documented as runbooks with specific commands and expected outcomes so any team member can execute a recovery under pressure.

Database engagements often involve an initial intensive project — schema redesign, performance audit, migration — followed by an ongoing lighter retainer for query review, capacity planning, and incident support. We offer fixed-price scoped projects, monthly retainer, and time-and-materials billing. All models include a dedicated account manager and transparent reporting.