Data invariants
Enforce critical uniqueness, relationships, and allowed values close to the data.
Relational data architecture
PostgreSQL Database Engineering.
PostgreSQL is more than storage: constraints, transactions, indexes, and operational controls are part of the application’s correctness model.
How we use it
We use PostgreSQL for systems where relationships, transactional consistency, flexible querying, and long-lived operational data matter. The schema is designed around business invariants and access patterns, then tested against realistic volume.
Architecture decisions
The questions that shape the implementation
Index strategy
Design indexes for observed access paths and account for their write and storage cost.
Concurrency
Reason about isolation, locking, contention, and duplicate requests in critical workflows.
Recovery
Define backup, restoration, retention, and recovery objectives before an incident.
Engineering practice
What disciplined use looks like
Relational constraints
Explain-plan review
Connection pooling
Online migration patterns
Least-privilege database roles
Backup restoration exercises
Failure modes
What we actively design against
Missing constraints hidden by application validation
Indexes added without workload evidence
Unbounded queries and connection counts
Backups that have never been restored
Tenant boundaries enforced inconsistently
Appropriate use cases
ERP and CRM platforms
SaaS products
Financial workflows
Inventory systems
Reporting foundations
Audit-sensitive operational software
Technical questions
Can PostgreSQL support both transactions and reporting?
Often, yes at moderate scale. Workload isolation, replicas, materialized views, or a separate analytical platform may become appropriate as reporting complexity grows.
How do you approach database performance?
We begin with workload evidence, query plans, schema shape, indexes, connection behavior, and contention rather than applying generic tuning settings.
Related expertise