Database Architect

WEX•Lower Macungie Township, PA

1d•$165,800 - $204,400

About The Position

The Senior Database Architect owns data architecture across multiple domains, modernizing legacy database systems while designing AI-native data infrastructure. The role focuses on large-scale SQL Server environments with complex stored procedures containing embedded business logic, as well as semantic data models, vector databases, and retrieval-augmented generation (RAG) architectures that support AI agents and workflows. The architect establishes patterns for domain-driven data models, event-driven data flows, and canonical schemas across relational, NoSQL, analytical, and AI-optimized platforms. The role addresses the decomposition of difficult-to-test and tightly coupled SQL Server stored procedures by moving business logic into application services. At the same time, it designs purpose-built infrastructure for AI applications, including semantic models, embedding pipelines, vector databases, and knowledge graphs. Domain knowledge captured in legacy systems and canonical models will also support AI retrieval and reasoning. This is an AI-first role: the architect will use AI tools to accelerate database analysis and migration work and will design the data infrastructure AI systems depend on. The position partners with AI/ML, domain, and application architecture teams; contributes to enterprise data architecture standards; and mentors engineers. Success includes delivering modernization assessments and AI-assisted tools, establishing RAG and semantic modeling patterns, reducing stored procedure complexity, and supporting production agent workflows.

Requirements

8–12 years of database engineering and architecture experience, including significant experience with enterprise-scale SQL Server environments.
Deep SQL Server expertise, including T-SQL optimization, stored procedure design and refactoring, query plan analysis, indexing, and performance tuning.
Hands-on experience decomposing complex stored procedures and migrating business logic to application services.
Experience designing data solutions across relational platforms such as SQL Server and PostgreSQL, NoSQL platforms such as MongoDB and Cosmos DB, and analytical platforms such as Snowflake or data lakehouses.
Production experience implementing event-driven data patterns such as CDC, Kafka, outbox, event sourcing, and CQRS.
Data modeling expertise in canonical models, dimensional modeling, schema evolution, and extensible design.
Hands-on experience with at least one vector database, such as Pinecone, Weaviate, Milvus, pgvector, Azure AI Search, or a similar platform.
Understanding of RAG architecture, including embedding models, chunking, retrieval optimization, hybrid search, and reranking.
Experience designing semantic data structures for AI retrieval, such as knowledge representations, ontologies, or domain-specific schemas.
Understanding of embedding pipelines, including text preprocessing, embedding generation, vector indexing, and incremental updates.
Familiarity with LLM context requirements, including agent data needs, token constraints, and context-window optimization.
At least 2 years of active use of AI coding assistants for database work, with the ability to prompt effectively for SQL and data engineering tasks.
Experience building tools, scripts, or automation that use AI/LLM capabilities.
Familiarity with structured artifacts for AI consumption, such as documented schemas, annotated procedures, and context files.
Vision for AI-assisted database engineering and ability to build enabling tools.
Strong programming skills in at least one backend language: C#, Java, or Python.
Experience with cloud data services such as Azure SQL, Cosmos DB, Azure AI Search, Azure Synapse, Snowflake, or AWS equivalents.
Experience with infrastructure-as-code for data platforms, such as 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 such as HIPAA and PCI-DSS.

Nice To Haves

Background in healthcare, benefits, payments, or similarly regulated industries.
Experience building RAG systems or AI-powered search and retrieval applications.
Knowledge graph experience with 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.

Responsibilities

Analyze and decompose large SQL Server stored procedures with embedded business logic, and create strategies to move logic into domain services.
Design patterns that separate business rules from data access so stored procedures can serve as thin data-access layers.
Lead database refactoring aligned with domain-driven design, including bounded contexts, aggregates, and domain events.
Implement event-driven patterns such as change data capture, outbox patterns, and event sourcing where appropriate.
Optimize query performance, indexing strategies, and execution plans during modernization efforts.
Create migration playbooks and tools for engineering teams modernizing stored procedures.
Design semantic data models that capture domain knowledge for AI retrieval and reasoning.
Architect vector database solutions for RAG, including embedding strategies, chunking, similarity search optimization, and hybrid retrieval.
Design and implement embedding pipelines that prepare domain content for AI agents.
Establish knowledge graph patterns, including entity relationships, ontologies, and graph-based retrieval, where appropriate.
Define data architectures for AI agent context, including data needs, structure, freshness, and consistency.
Design evaluation frameworks for RAG quality, including retrieval accuracy, relevance scoring, and continuous-improvement feedback loops.
Design canonical data models and schemas aligned with business domains and extensibility needs.
Architect data solutions across SQL Server, PostgreSQL, MongoDB/Cosmos DB, Snowflake, and vector database platforms.
Design event-driven data flows using Kafka-based streaming, materialized views, CQRS patterns, and real-time synchronization.
Establish data platform patterns for pipelines, ETL/ELT orchestration, data quality, and observability.
Define data residency, partitioning, and multi-region strategies for performance and compliance.
Create reference architectures for data patterns that domain teams can adopt.
Use AI coding assistants to accelerate stored procedure analysis, refactoring, and migration.
Build AI-powered database engineering tools, such as stored procedure analyzers, schema documentation generators, migration assistants, and query optimization recommenders.
Create AI-consumable artifacts, including structured documentation, annotated schemas, and context files.
Author database architecture skills that encode patterns, constraints, and best practices for AI-assisted development.
Develop prompts, workflows, and tooling to help engineering teams use AI for database modernization.
Partner with AI/ML teams to align data architecture with agent and workflow requirements.
Collaborate with domain teams to understand data needs and design solutions aligned with domain ownership.
Work with application architects to support service-oriented and event-driven designs.
Contribute to Enterprise Architecture Council standards for data architecture, modeling conventions, and technology selection.
Mentor engineers in database design, optimization, semantic modeling, and AI data infrastructure.

Benefits

Health insurance
Dental insurance
Vision insurance
Retirement savings plan
Paid time off
Health savings account
Flexible spending accounts
Life insurance
Disability insurance
Tuition reimbursement
Quarterly or annual bonus eligibility for non-sales roles, subject to role and applicable plan