Database Design & Architecture

Overview

What we deliver

We design relational and non-relational databases with clean schemas, indexing, and partitioning so your product stays fast as data grows.

We work with engineering and product teams to design databases that perform well under real workloads and stay manageable as the product evolves. Our engineers review business entities, query patterns, and growth projections, then propose a schema in PostgreSQL, MySQL, MongoDB, or a hybrid model. We define indexes, constraints, and partitioning strategies that match how the system will be queried. We address operational concerns from day one. That includes backup and restore plans, read replica setup, caching with Redis, and migration scripts that can run safely on production. We also document the model, data lineage, and ownership so internal teams can build new features without guesswork. Whether you are launching a new product, splitting a monolith, or fixing performance issues in an existing system, we deliver a data architecture that supports current needs and a clear path for future scale.

Fit Check

Built for teams like yours

Who it's for

Startups designing a new product
Teams splitting a monolithic database
Companies migrating between database engines
Engineering leaders fixing performance issues
Analytics teams building reporting layers

Pain points we solve

Slow queries under growing load
Poor schema decisions blocking features
Data inconsistencies across services
Painful migrations and downtime risk
Lack of documentation and ownership

What's included

Capabilities

Everything we cover in this engagement.

Requirements and query analysis
Logical and physical schema design
Indexing and partitioning strategy
Migration planning and scripts
Replication and backup setup
Caching layer design
Performance tuning
Data dictionary and documentation

How we work

Our process

A clear, predictable path from kickoff to outcomes.

01

Discovery

We review entities, access patterns, and growth plans.

02

Model

We draft logical and physical schemas with key constraints.

03

Optimize

We define indexes, partitions, and caching strategies.

04

Migrate

We write and test migration scripts with rollback paths.

05

Operate

We set up backups, monitoring, and documentation.

What you get

Deliverables & outcomes

What you get

Logical and physical schema diagrams
Migration scripts with rollback paths
Indexing and partitioning plan
Backup and replication setup
Performance baseline report
Data dictionary and ownership map

Outcomes you can expect

Faster query performance
Stable behavior at higher loads
Safer migrations with less downtime
Clearer ownership of data domains
Lower long-term operational cost

Timeline

4 to 12 weeks per data domain

Engagement

Monthly retainer, Project, Sprint

Tools we use

PostgreSQL, MySQL, MongoDB, Redis, dbt

KPIs we track

Query P95 latency, Storage growth rate, Failed migration count, Replication lag, Index hit rate

Client stories

What clients say

"

My books were 90 days behind and I was avoiding my accountant. They cleaned up nine months of mis-categorized Shopify and Stripe entries, set up proper rules in QuickBooks, and now my close lands on day four of every month. First time in three years I opened a P&L without wincing. Cash forecasting actually makes sense now.

D.R.

"

Our SDRs were spending two hours a day copying lead data between Salesforce, Outreach, and a Google Sheet nobody owned. They mapped the whole flow, stitched it together in n8n, and added a dedupe step we did not even know we needed. Got 38 hours a week back across the team. The SDRs were the ones who pushed to expand it further.

Rebecca F.

Proof