Senior Database Architect

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

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

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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

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

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

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

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

AI Data Infrastructure & Semantic Modeling

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

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

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

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

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

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

Modern Data Platform Architecture

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

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

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

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

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

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

AI-First Database Engineering

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

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

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

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Author database architecture skills that encode patterns, constraints, and best pra