PostgreSQL
Designed
For Scale.
Schema design, migrations, RLS multi-tenancy, pgvector AI search, and asyncpg integration. Production PostgreSQL from day one — not refactored when things break.
< DESIGN_MY_SCHEMA />What I Design & Build
< SCHEMA_DESIGN />
Normalized tables, JSONB for flexible fields, proper foreign keys, check constraints, and indexes that match your actual query patterns.
< MULTI-TENANT_RLS />
Row-Level Security policies that enforce tenant isolation at the database level. No application-layer WHERE clauses that can be bypassed.
< AI_VECTOR_SEARCH />
pgvector extension for semantic similarity search. Knowledge base search, product recommendations, and AI RAG without a separate vector database.
PostgreSQL Skills I Bring
Schema Design
Relational normalization, JSONB for semi-structured data, proper indexing strategy, and constraint design that enforces business rules at the DB level.
Row-Level Security
PostgreSQL RLS for multi-tenant apps. Tenant ID propagated via JWT claim, enforced by policy. Even raw psql access cannot see other tenant data.
pgvector
Vector embeddings stored in PostgreSQL. Semantic search, RAG pipelines, and AI recommendation engines without Pinecone or Weaviate.
Performance Tuning
EXPLAIN ANALYZE, partial indexes, covering indexes, connection pooling with PgBouncer, and query optimization for tables with 50M+ rows.
Migrations
Version-controlled schema migrations with zero-downtime strategies: adding columns, creating indexes concurrently, and backfilling data safely.
Replication & Backup
Logical replication for read replicas, pg_dump automation, and point-in-time recovery configuration for production databases.
My PostgreSQL Philosophy
The database schema is the most important architectural decision in any system. Get it wrong and every feature built on top inherits the debt. Get it right and the application layer becomes almost trivial.
I start every project with a schema document before writing any application code. The questions I answer first: What are the entities? What are the relationships? Which fields need to be indexed? Where can JSONB replace a normalized table without sacrificing query ability?
On JSONB vs Relational: I use JSONB for data where the shape varies by row — page content, block configurations, metadata. I use normalized tables for data that needs to be queried, filtered, or joined — users, orders, leads. The combination gives you document database flexibility with relational integrity.
On ORM vs asyncpg: For high-throughput backends I skip SQLAlchemy and write SQL directly with asyncpg. The reason is simple: ORMs abstract the query, which means you cannot optimize what you cannot see. asyncpg forces you to write the query you mean, and the performance difference at scale is 3–5x.
Database Mistakes I Fix on Every Audit
These problems appear in 80% of the codebases I review. They compound over time into multi-day outages.
- ⚠ Missing indexes on foreign keys — table scans on every JOIN
- ⚠ N+1 queries from ORMs — 500 queries per page load
- ⚠ No connection pooling — DB connections exhausted under load
- ⚠ Sequences not cached — INSERT bottleneck at 10k rows/sec
- ⚠ RLS missing — application WHERE clause as only tenant barrier
Database Architecture Review
Share your current schema. I'll find the bottlenecks and design the fix.
Your Database Is the Foundation.
Most performance problems are schema problems. Let's fix the foundation.
Book a Schema Review