Database Architect

Role Scope

Owns data architecture across multiple domains, with dual focus: (1) modernizing legacy database systems—particularly large-scale SQL Server environments with complex stored procedures containing embedded business logic, and (2) designing AI-native data infrastructure—semantic data models, vector databases, and RAG architectures that power AI agents and workflows. Establishes patterns for domain-driven data models, event-driven data flows, and canonical schemas across relational, NoSQL, analytical, and AI-optimized data platforms.

The Problem We're Solving

We have two interconnected data architecture challenges.

Legacy modernization: SQL Server stored procedures that have grown to thousands of lines, with business logic deeply embedded in T-SQL that should live in application services. These procedures are difficult to test, impossible to reuse, and create tight coupling between business rules and data access. They slow down development, complicate domain ownership, and resist modernization efforts.

AI data infrastructure: We're building AI agents and agentic workflows that require purpose-built data architecture—semantic models that capture domain knowledge, vector databases for retrieval-augmented generation (RAG), embedding pipelines, and knowledge graphs that give AI systems the context they need to operate effectively. This isn't about bolting AI onto existing data systems; it's about designing data infrastructure that makes AI-native applications possible.

These challenges intersect: the domain knowledge locked in legacy systems becomes training data and context for AI agents. The canonical models we build for modernization become the semantic foundation for AI retrieval. We need database architects who can work across both dimensions—modernizing legacy while building for an AI-native future.

The Opportunity

We are seeking a Senior Database Architect who combines deep expertise in legacy database systems with forward-looking vision for AI-native data architecture. You'll lead the decomposition of complex stored procedures while simultaneously designing the vector databases, embedding strategies, and semantic models that power our AI agents and workflows.

This is an AI-first role in two senses: you'll leverage AI to accelerate your own work (stored procedure analysis, migration generation, schema documentation), and you'll design the data infrastructure that AI systems depend on. If you're excited about both solving hard legacy database problems and architecting the data layer for AI-native applications, this role is for you.

What You'll Do

Legacy Database Modernization

Analyze and decompose large SQL Server stored procedures (1,000+ lines) with embedded business logic, creating migration strategies that extract logic into domain services

Design patterns for separating business rules from data access, enabling stored procedures to become thin data-access layers while business logic moves to application services

Lead refactoring efforts that align database structures with domain-driven design: bounded contexts, aggregates, and domain events

Implement event-driven patterns that decouple systems from direct database dependencies: change data capture, outbox patterns, event sourcing where appropriate

Optimize query performance, indexing strategies, and execution plans as part of modernization efforts

Create migration playbooks and tooling that engineering teams can apply to their own stored procedure modernization

AI Data Infrastructure & Semantic Modeling

Design semantic data models that capture domain knowledge in structures optimized for AI retrieval and reasoning

Architect vector database solutions for RAG implementations: embedding strategies, chunking approaches, similarity search optimization, and hybrid retrieval patterns

Design and implement embedding pipelines that transform domain content into vector representations suitable for AI agent consumption

Establish knowledge graph patterns where appropriate: entity relationships, ontologies, and graph-based retrieval for complex domain reasoning

Define data architectures for AI agent context: what data agents need, how it's structured, how freshness and consistency are maintained

Design evaluation frameworks for RAG quality: retrieval accuracy, relevance scoring, and feedback loops for continuous improvement

Modern Data Platform Architecture

Design canonical data models and schemas that are flexible, extensible, and aligned with business domain concepts

Architect data solutions across multiple platforms: SQL Server, PostgreSQL, MongoDB/Cosmos DB, Snowflake, and vector databases (Pinecone, Weaviate, pgvector, Azure AI Search)

Design event-driven data flows: Kafka-based event streaming, materialized views, CQRS patterns, and real-time data synchronization

Establish data platform infrastructure patterns: data pipelines, ETL/ELT orchestration, data quality frameworks, and observability

Define data residency, partitioning, and multi-region strategies for performance and compliance

Create reference architectures for common data patterns that domain teams can adopt

AI-First Database Engineering

Leverage AI coding assistants (GitHub Copilot, Cursor, Claude Code) to accelerate stored procedure analysis, refactoring, and migration

Build AI-powered tools for database engineering: automated stored procedure analysis, schema documentation generators, migration assistants, and query optimization recommenders

Create AI-consumable artifacts: structured documentation, annotated schemas, and context files that enable AI agents to understand and work with database systems

Author database architecture skills that encode patterns, constraints, and best practices for AI-assisted development

Develop prompts, workflows, and tooling that help engineering teams apply AI effectively to database modernization tasks

Cross-Domain Leadership

Partner with AI/ML teams to ensure data architecture supports agent and workflow requirements

Collaborate with domain teams to understand their data requirements and design solutions aligned with domain ownership

Work with application architects to ensure data architecture supports service-oriented and event-driven designs

Contribute to Enterprise Architecture Council (EAC) standards for data architecture, modeling conventions, and technology selection

Mentor engineers on database design, optimization, semantic modeling, and AI data infrastructure

What You'll Bring

Required Experience

8–12 years in database engineering and architecture, with significant experience in enterprise-scale SQL Server environments

Deep SQL Server expertise: T-SQL optimization, stored procedure design and refactoring, query plan analysis, indexing strategies, and performance tuning

Hands-on modernization experience: track record of decomposing complex stored procedures and migrating business logic to application services

Multi-platform data architecture: experience designing solutions across relational (SQL Server, PostgreSQL), NoSQL (MongoDB, Cosmos DB), and analytical (Snowflake, data lakehouse) platforms

Event-driven data patterns: CDC, Kafka, outbox pattern, event sourcing, CQRS—practical experience implementing these in production

Data modeling expertise: canonical models, dimensional modeling, schema evolution, and designing for extensibility

AI & Semantic Data Competencies

Vector database experience: hands-on with at least one vector DB (Pinecone, Weaviate, Milvus, pgvector, Azure AI Search, or similar)

RAG architecture understanding: embedding models, chunking strategies, retrieval optimization, hybrid search, and reranking patterns

Semantic modeling: experience designing data structures optimized for AI retrieval—knowledge representation, ontologies, or domain-specific schemas for AI consumption

Understanding of embedding pipelines: text preprocessing, embe