Data modelling
Review entities, relationships, normalization, constraints, indexes, and ownership. Design schemas around real access patterns while protecting referential integrity and avoiding premature complexity.
Services · Kweezi Perez Christopher
Data Engineering
Good software depends on good data foundations. I design data systems around correctness, performance, observability, and the way applications actually access information.
Based in Kampala, Uganda; available for remote collaboration with clients internationally.
What I work on
- Relational database design and normalization
- PostgreSQL, SQL, MongoDB, and application data models
- Data pipelines and API-driven ingestion
- Query optimization, indexing, pagination, and caching strategies
- Backups, migrations, constraints, and operational reliability
Engineering principles
- Keep data integrity close to the data
- Measure query patterns before optimizing infrastructure
- Bound expensive operations and large responses
- Make migrations repeatable and recoverable
- Design for growth without adding complexity prematurely
Make data dependable before making it bigger
Data engineering is about preserving meaning and correctness as information moves through an application. A sound design considers where data is created, which system owns it, how it is validated, who can access it, how it changes, and how the system recovers when a job or deployment fails.
Pipelines and integration
Plan ingestion, transformation, validation, deduplication, retries, and idempotency. Make data movement observable so incomplete or repeated jobs can be diagnosed instead of silently corrupting downstream results.
Performance and recovery
Measure slow queries, examine execution plans, tune indexes and pagination, and consider caching only where it fits. Pair migrations with backup, restore, and rollback procedures appropriate to the system.
Typical deliverables
- Entity relationship models, schema definitions, constraints, and migration scripts.
- Documented ingestion and transformation workflows with validation rules.
- Query and index recommendations supported by measured access patterns.
- Backup, restore, retention, and data-access guidance.
- Tests and monitoring for critical data integrity and pipeline behaviour.
What if the database already exists?
An existing system can be assessed before changes are made. The review can identify risky schema assumptions, expensive queries, missing constraints, migration hazards, and operational gaps. Recommendations should be prioritized by observed impact and risk, with a staged plan for changes that could affect live data.
Do you need to replace your database? Not necessarily. The right outcome may be better modelling, indexes, queries, connection management, or backup practices—not a migration to a different platform.
Related technical insights
Go deeper into the engineering practices and production trade-offs behind this service.
Work with me
If you have a product, workflow, system, or technical problem that needs to become real software, start with the requirements and constraints. I can help turn them into an implementation plan and a production-focused build.