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Technology Expertise

PostgreSQL
Development.

Expert PostgreSQL development with deep production experience. From architecture decisions to performance optimization, I help teams build systems that scale.

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Expertise Level

PostgreSQL administrator and developer since version 9.4 (2014). Experience with logical replication, partitioning strategies, and performance tuning for billion-row tables. Designed schemas handling 50K+ transactions per second, optimized queries from minutes to milliseconds using EXPLAIN ANALYZE, and implemented zero-downtime migrations for production systems.

When to Use PostgreSQL

Applications requiring ACID compliance where data integrity is non-negotiable (financial, healthcare, legal)

Complex queries with JOINs across multiple tables—Postgres query planner handles 10-way joins efficiently

Full-text search requirements using tsvector/tsquery before reaching for Elasticsearch

Geospatial applications with PostGIS extension for location queries, polygon intersections, and routing

Time-series data with TimescaleDB extension when you need relational features alongside time-series optimization

JSON document storage with JSONB when you need NoSQL flexibility with relational guarantees

Multi-tenant SaaS applications using row-level security (RLS) for tenant isolation

Best Practices

Use UUIDs (uuid_generate_v4) for primary keys in distributed systems; BIGSERIAL for single-instance performance

Implement proper indexing strategy: B-tree for equality/range, GIN for arrays/JSONB/full-text, GiST for geometry

Use CTEs (WITH clauses) for complex queries but understand they're optimization fences in Postgres <12

Leverage LISTEN/NOTIFY for real-time updates instead of polling; pairs well with pg_notify triggers

Configure pgBouncer for connection pooling—transaction mode for serverless, session mode for prepared statements

Use pg_stat_statements extension to identify slow queries and index opportunities

Implement database migrations with version control (Prisma Migrate, golang-migrate, or Flyway)

Common Pitfalls to Avoid

Not understanding MVCC—UPDATE creates new row versions, leading to table bloat without regular VACUUM

Using SELECT * in production—always specify columns to avoid fetching unnecessary data and breaking on schema changes

Forgetting that LIKE '%term%' can't use indexes—use trigram indexes (pg_trgm) or full-text search instead

Creating indexes without CONCURRENTLY flag—blocks writes on large tables for minutes or hours

Not setting connection pool size correctly—max_connections default (100) is too low for production

Using ORM-generated queries without EXPLAIN ANALYZE—ORMs generate N+1 queries and inefficient JOINs

Storing large blobs in tables instead of external storage—bloats WAL and slows backups

Ideal Project Types

Financial applications requiring ACID guarantees
Multi-tenant SaaS platforms
Geospatial and mapping applications
Analytics and reporting systems
Content management systems
E-commerce platforms with complex inventory

Complementary Technologies

pgBouncer (connection pooling)
TimescaleDB (time-series extension)
PostGIS (geospatial extension)
pg_cron (scheduled jobs)
Prisma or Drizzle (type-safe ORM)
Supabase or Neon (managed Postgres)

Real-World Example

Case Study

The PhotoKeep Pro billing system showcases PostgreSQL's strengths. The schema uses row-level security (RLS) for multi-tenant isolation—each customer's data is automatically filtered by tenant_id without application code changes. Credits and transactions use SERIALIZABLE isolation level to prevent double-spending race conditions. I implemented a JSONB column for storing ML model parameters, allowing flexible configuration per restoration job while maintaining queryability (CREATE INDEX ON jobs USING GIN (params jsonb_path_ops)). For usage analytics, I partitioned the events table by month using declarative partitioning, with a pg_cron job that creates future partitions and drops old ones. The subscription status uses a materialized view refreshed every 15 minutes, joining Stripe webhook events with internal user data. Query times for the admin dashboard dropped from 12 seconds to 80ms after adding partial indexes on status columns where status = 'active'.

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PostgreSQL project.

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