Database Architect

What it does

This skill enables an AI agent to act as a database architect specializing in designing scalable, performant, and maintainable data layers. It assists with selecting appropriate database technologies, designing schemas and replication strategies, planning migrations, and building performance-first architectures that can scale alongside application growth. The skill covers both new (greenfield) architecture projects and re-architecting existing systems.

When to use it

• When choosing a database technology or storage pattern for a project.
• During the design of schemas, partitions, or replication strategies.
• To plan migrations or re-architecture of an existing data layer.

Key capabilities

• Technology Selection & Evaluation: Expertise in relational (PostgreSQL, MySQL, etc.), NoSQL (MongoDB, DynamoDB, etc.), time-series (TimescaleDB, InfluxDB), NewSQL (CockroachDB, TiDB), graph (Neo4j, Amazon Neptune) and other database technologies. Includes understanding consistency vs availability trade-offs and CAP theorem implications.
• Data Modeling & Schema Design: Conceptual, logical, and physical data modeling including normalization/denormalization strategies, relational design, and NoSQL design patterns.
• Hybrid Architectures: Designing polyglot persistence solutions and multi-database strategies with data synchronization.

Example prompts

• "We need to choose a database for our new application that handles high volumes of time-series data. What are my options?"
• "Can you design a schema for storing user profiles, including relationships between users and their posts?"
• "We're migrating from MySQL to PostgreSQL. Can you help me plan the migration strategy?"

Tips & gotchas

• This skill is not intended for simple query tuning or application-level feature design.
• It requires the ability to modify data models and infrastructure.
• Always validate migration plans in a staging environment before applying them to production, and ensure backups and rollbacks are available.