Senior Database Architect

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

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

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

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, embedding generation, vector indexing, and incremental updates

Familiarity with LLM context requirements: what data AI agents need, token constraints, context window optimization

2+ years actively using AI coding assistants for database work; deep understanding of how to prompt effectively for SQL and data engineering tasks

Experience building tools, scripts, or automation that leverage AI/LLM capabilities

Familiarity with structured artifact creation for AI consumption: documented schemas, annotated procedures, context files

Vision for AI-assisted database engineering and ability to build tooling that enables it

Strong programming skills in at least one backend language (C#, Java, Python) for building migration tooling, embedding pipelines, and services

Cloud data services experience: Azure SQL, Cosmos DB, Azure AI Search, Azure Synapse, Snowflake, or AWS equivalents

Infrastructure-as-code for data platforms: Terraform, ARM/Bicep, or CloudFormation

Understanding of domain-driven design and how data architecture supports bounded contexts

Familiarity with data governance, lineage, and compliance requirements (HIPAA, PCI-DSS)

Background in healthcare, benefits, payments, or similarly regulated industries

Experience building RAG systems or AI-powered search/retrieval applications

Knowledge graph experience: Neo4j, Amazon Neptune, or similar graph databases

Contributions to database tooling, AI/ML data infrastructure, or open-source projects

Experience mentoring engineers or leading database/data architecture communities of practice

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

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.