Job description

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, 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 The base pay range represents the anticipated low and high end of the pay range for this position. Actual pay rates will vary and will be based on various factors, such as your qualifications, skills, competencies, and proficiency for the role. Base pay is one component of WEX's total compensation package. Most sales positions are eligible for commission under the terms of an applicable plan. Non-sales roles are typically eligible for a quarterly or annual bonus based on their role and applicable plan. WEX's comprehensive and market competitive benefits are designed to support your personal and professional well-being. Benefits include health, dental and vision insurances, retirement savings plan, paid time off, health savings account, flexible spending accounts, life insurance, disability insurance, tuition reimbursement, and more. For more information, check out the "About Us" section.

Requirements

Must-haves

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

Nice-to-haves

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

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aisqlazuremlllmjavapythonaws

Platform: workday
Effort: long

Benefits

Benefits detected in the listing.

Dental

Vision

Paid Time Off