PostgreSQL Developer
for Fintech

PostgreSQL is the default choice for fintech because it treats data integrity as non-negotiable. When you're processing financial transactions, you cannot accept 'eventual consistency' or 'best effort delivery.' PostgreSQL's ACID guarantees mean that when a transaction commits, the data is durably stored, consistent with all constraints, and visible to subsequent queries. SERIALIZABLE isolation level goes further, preventing the subtle race conditions that can occur even with REPEATABLE READ. Two concurrent transactions checking a balance and then debiting it will never both succeed if the combined debits exceed the balance... the database detects the serialization anomaly and rolls back one transaction. This isn't something you can implement reliably in application code. The financial data model maps naturally to relational databases. Accounts have balances. Transactions debit one account and credit another. Constraints ensure debits equal credits. Foreign keys maintain referential integrity. These aren't limitations... they're features that catch bugs before they become reconciliation nightmares. The surrounding ecosystem hardens the stack further. pgcrypto handles column-level encryption for PCI-scoped data. PgBouncer in transaction-pooling mode keeps connection counts sane under serverless load. Logical replication via pglogical streams a tamper-evident copy of the WAL into cold storage for SOX retention. Partitioning by transaction_date keeps indexes small and makes seven-year retention purges a single DETACH PARTITION instead of a locking DELETE. Stripe, Adyen, and Robinhood run this stack for a reason... it scales horizontally with read replicas, handles thousands of transactions per second per primary, and carries no per-core licensing cost that distorts architecture decisions at scale.

Fintech database projects start with understanding the transaction model and regulatory requirements. What are the transaction types? What consistency guarantees are required? What audit and retention requirements apply? These answers shape the schema design. I architect schemas that encode financial constraints directly in the database. Balance columns have CHECK constraints preventing negative values (for accounts that shouldn't go negative). Transaction tables have constraints ensuring debits equal credits. Idempotency keys prevent duplicate processing. These constraints are defense-in-depth against application bugs. For multi-tenant fintech, row-level security is my default pattern. Every query automatically filters by tenant without application code changes. Even a SQL injection attack can't access another tenant's data because the database itself enforces the boundary. This dramatically simplifies security audits. Performance testing uses realistic transaction volumes from day one to ensure the database handles peak load with margin to spare. Partitioning strategy gets locked in before the first production write... retrofitting partition keys on a billion-row ledger is a multi-week migration that freezes product work. I partner with the engineering team on migration discipline: every schema change ships through Flyway or Sqitch with a reviewed rollback path, pg_stat_statements gates merges with a slow-query budget, and CI runs EXPLAIN ANALYZE against a production-sized snapshot to catch sequential scans before they cause 2AM pages. The output is a database that passes SOX audits without theatrics and handles the next 10x in volume without a rewrite.