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

Analyze and decompose large SQL Server stored procedures (1,000+ lines) with embedded business logic, creating migration strategies that extract logic into domain servicesDesign patterns for separating business rules from data access, enabling stored procedures to become thin data-access layers while business logic moves to application servicesLead refactoring efforts that align database structures with domain-driven design: bounded contexts, aggregates, and domain eventsImplement event-driven patterns that decouple systems from direct database dependencies: change data capture, outbox patterns, event sourcing where appropriateOptimize query performance, indexing strategies, and execution plans as part of modernization effortsCreate 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 reasoningArchitect vector database solutions for RAG implementations: embedding strategies, chunking approaches, similarity search optimization, and hybrid retrieval patternsDesign and implement embedding pipelines that transform domain content into vector representations suitable for AI agent consumptionEstablish knowledge graph patterns where appropriate: entity relationships, ontologies, and graph-based retrieval for complex domain reasoningDefine data architectures for AI agent context: what data agents need, how it's structured, how freshness and consistency are maintainedDesign 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 conceptsArchitect 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 synchronizationEstablish data platform infrastructure patterns: data pipelines, ETL/ELT orchestration, data quality frameworks, and observabilityDefine data residency, partitioning, and multi-region strategies for performance and complianceCreate 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 migrationBuild AI-powered tools for database engineering: automated stored procedure analysis, schema documentation generators, migration assistants, and query optimization recommendersCreate AI-consumable artifacts: structured documentation, annotated schemas, and context files that enable AI agents to understand and work with database systemsAuthor database architecture skills that encode patterns, constraints, and best practices for AI-assisted developmentDevelop 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 requirementsCollaborate with domain teams to understand their data requirements and design solutions aligned with domain ownershipWork with application architects to ensure data architecture supports service-oriented and event-driven designsContribute to Enterprise Architecture Council (EAC) standards for data architecture, modeling conventions, and technology selectionMentor 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 environmentsDeep SQL Server expertise: T-SQL optimization, stored procedure design and refactoring, query plan analysis, indexing strategies, and performance tuningHands-on modernization experience: track record of decomposing complex stored procedures and migrating business logic to application servicesMulti-platform data architecture: experience designing solutions across relational (SQL Server, PostgreSQL), NoSQL (MongoDB, Cosmos DB), and analytical (Snowflake, data lakehouse) platformsEvent-driven data patterns: CDC, Kafka, outbox pattern, event sourcing, CQRS—practical experience implementing these in productionData 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 patternsSemantic modeling: experience designing data structures optimized for AI retrieval—knowledge representation, ontologies, or domain-specific schemas for AI consumptionUnderstanding of embedding pipelines: text preprocessing, embedding generation, vector indexing, and incremental updatesFamiliarity with LLM context requirements: what data AI agents need, token constraints, context window optimization

AI-Native Engineering Practices

2+ years actively using AI coding assistants for database work; deep understanding of how to prompt effectively for SQL and data engineering tasksExperience building tools, scripts, or automation that leverage AI/LLM capabilitiesFamiliarity with structured artifact creation for AI consumption: documented schemas, annotated procedures, context filesVision for AI-assisted database engineering and ability to build tooling that enables it

Technical Depth

Strong programming skills in at least one backend language (C#, Java, Python) for building migration tooling, embedding pipelines, and servicesCloud data services experience: Azure SQL, Cosmos DB, Azure AI Search, Azure Synapse, Snowflake, or AWS equivalentsInfrastructure-as-code for data platforms: Terraform, ARM/Bicep, or CloudFormationUnderstanding of domain-driven design and how data architecture supports bounded contextsFamiliarity with data governance, lineage, and compliance requirements (HIPAA, PCI-DSS)

Preferred Experience

Background in healthcare, benefits, payments, or similarly regulated industriesExperience building RAG systems or AI-powered search/retrieval applicationsKnowledge graph experience: Neo4j, Amazon Neptune, or similar graph databasesContributions to database tooling, AI/ML data infrastructure, or open-source projectsExperience mentoring engineers or leading database/data architecture communities of practice

What Success Looks Like

In 90 days: Completed assessment of priority stored procedure modernization targets and AI data infrastructure needs; delivered first AI-assisted analysis tooling; established vector database patterns for initial RAG implementations

In 6 months: Led decomposition of at least one major stored procedure system; semantic data models and RAG architecture patterns established and being adopted; AI-powered database engineering tools in active use by teams

In 12 months: Measurable reduction in stored procedure complexity across priority systems; AI data infrastructure supporting production agent workflows; recognized as the go-to expert for both database modernization and AI-native data architecture

Why This Role Matters

Data architecture is being transformed from two directions simultaneously.

From the legacy side: business logic buried in stored procedures creates invisible dependencies that resist refactoring. Traditional approaches to database modernization are slow, manual, and error-prone—but AI can analyze thousands of lines of T-SQL, identify patterns, and accelerate migrations in ways that weren't possible before.

From the AI side: agents and workflows need purpose-built data infrastructure. The semantic models, vector databases, and knowledge representations you design will determine how effectively AI can reason about our domains. This isn't a nice-to-have capability; it's foundational to our AI-native engineering strategy.

You'll work at the intersection of these transformations—solving hard legacy problems while building the data infrastructure that makes AI-native applications possible. The patterns you establish will shape how we approach data architecture across the enterprise.