AI-First Data Architect for Legacy Modernization

WEX Inc.; United States; On-site; USD 150,000 - 230,000; Full time

Job summary

WEX Inc. seeks a Senior Database Architect who combines deep SQL Server expertise with a vision for AI-native data infrastructure. You will modernize legacy stored procedures and migrate logic to domain services to enable scalable AI workflows.

You will design vector databases, embedding pipelines, and semantic models for AI agents, while shaping a cross‑platform data platform across SQL Server, PostgreSQL, MongoDB/Cosmos DB, Snowflake and vector stores.

Qualifications

• 8–12 years in database engineering and architecture.
• Deep SQL Server expertise: T-SQL optimization, stored procedures, and performance tuning.
• Experience decomposing complex stored procedures and migrating logic to services.
• Multi-platform data architecture across relational, NoSQL, and analytical platforms.
• Event-driven patterns: CDC, outbox, CQRS, and related practices.

Responsibilities

• Analyze and decompose large SQL Server stored procedures (1,000+ lines) with embedded business logic.
• Design patterns to separate business rules from data access and migrate logic to application services.
• Lead refactoring to align database structures with domain-driven design and events.
• Implement event-driven patterns to decouple systems from direct DB dependencies.
• Optimize queries, indexing, and execution plans as part of modernization.
• Create migration playbooks and tooling for stored procedure modernization.

Skills

SQL Server

T-SQL

Stored procedures

Data modeling

Event-driven

Vector databases

RAG architecture

Semantic modeling

Embedding pipelines

Cross-platform design

Tools

Kafka

Snowflake

MongoDB

Cosmos DB

Job description

WEX Inc. seeks a Senior Database Architect who combines deep SQL Server expertise with a vision for AI-native data infrastructure. You will modernize legacy stored procedures and migrate logic to domain services to enable scalable AI workflows.

You will design vector databases, embedding pipelines, and semantic models for AI agents, while shaping a cross‑platform data platform across SQL Server, PostgreSQL, MongoDB/Cosmos DB, Snowflake and vector stores.