Senior Database Architect

Legacy Database Modernization

AI Data Infrastructure & Semantic Modeling

Modern Data Platform Architecture

AI-First Database Engineering

Cross-Domain Leadership

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, embedding generation, vector indexing, and incremental updates
• Familiarity 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 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
• Technical Depth
• 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)
• Preferred Experience
• 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

AI & Semantic Data Competencies

AI-Native Engineering Practices

Technical Depth

Preferred Experience

Responsibilities

• 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