Data & Database Engineering

When reports disagree, imports fail silently, or a page takes ten seconds to load, the cause is usually the data layer. Schemas that grew without a plan, migrations nobody tested, and spreadsheets acting as databases all make the business harder to run and AI harder to adopt.

We design schemas and data models, run historical imports and migrations, build validation and data-quality controls, and create reporting and export pipelines. Performance work finds and fixes the slow queries and indexes that hold applications back. Architecture and delivery stay aligned, so the database remains an asset as records, teams, and reporting needs grow.

This is also the foundation for automation and AI. Clean, documented, validated data is what makes a forecast trustworthy or a retrieval system accurate. The Syntonyl Data Foundation package bundles this work into a defined scope for teams starting fresh.

What’s included

• Schema and data-model design
• Historical imports and migrations
• Validation and data-quality controls
• Reporting and export pipelines
• Query and index performance improvements
• Documentation and a data ownership plan

A good fit when

• Reports disagree or require manual cleanup.
• You are moving off spreadsheets or an aging system.
• Your application slows down as data grows.
• You want to use AI or forecasting and need reliable inputs.