Senior Database Architect

About This Opportunity

We are representing a confidential fintech organization seeking a Senior Database Architect who bridges legacy modernization with AI-native data infrastructure. This role combines deep expertise in enterprise database systems with forward-looking vision for vector databases, semantic modeling, and AI-powered data architecture. You'll lead the decomposition of complex stored procedures while simultaneously designing the data layer that powers AI agents and retrieval workflows.

This is an AI-first role in two dimensions: you'll leverage AI to accelerate database modernization work, and you'll architect the data infrastructure that AI systems depend on. If you're energized by both solving hard legacy database challenges and building the semantic data foundations for AI-native applications, we'd like to speak with you.

Responsibilities

Legacy Database Modernization

• Analyze and decompose large 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 layers
• Lead refactoring efforts aligned with domain-driven design principles: bounded contexts, aggregates, and domain events
• Implement event-driven patterns including change data capture, outbox patterns, and event sourcing where appropriate
• Optimize query performance, indexing strategies, and execution plans
• Create migration playbooks and tooling for engineering teams

AI Data Infrastructure & Semantic Modeling

• Design semantic data models 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
• Establish knowledge graph patterns where appropriate for complex domain reasoning
• Define data architectures for AI agent context: structure, freshness, and consistency requirements
• Design evaluation frameworks for RAG quality and continuous improvement

Modern Data Platform Architecture

• Design canonical data models aligned with business domain concepts
• Architect solutions across SQL Server, PostgreSQL, MongoDB, Snowflake, and vector databases (Pinecone, Weaviate, pgvector, Azure AI Search)
• Design event-driven data flows: Kafka-based streaming, materialized views, CQRS patterns, real-time synchronization
• Establish data platform infrastructure: pipelines, ETL/ELT orchestration, data quality frameworks, observability
• Define data residency, partitioning, and multi-region strategies

AI-Assisted Database Engineering

• Leverage AI coding assistants to accelerate stored procedure analysis, refactoring, and migration
• Build AI-powered tools for database engineering: automated analysis, schema documentation generators, migration assistants
• Create AI-consumable artifacts: structured documentation, annotated schemas, context files
• Develop prompts and workflows that help teams apply AI effectively to database modernization

Cross-Domain Leadership

• Partner with AI/ML teams to ensure data architecture supports agent and workflow requirements
• Collaborate with domain teams to design solutions aligned with domain ownership
• Work with application architects on service-oriented and event-driven designs
• Contribute to enterprise architecture standards for data architecture and technology selection
• Mentor engineers on database design, optimization, semantic modeling, and AI data infrastructure

What We're Looking For

Required Experience

• 8–12 years in database engineering and architecture with enterprise-scale SQL Server environments
• Deep SQL Server expertise: T-SQL optimization, stored procedure design and refactoring, query plan analysis, indexing strategies, performance tuning
• Proven track record decomposing complex stored procedures and migrating business logic to application services
• Multi-platform data architecture across relational (SQL Server, PostgreSQL), NoSQL (MongoDB, Cosmos DB), and analytical (Snowflake, data lakehouse) platforms
• Production experience with event-driven data patterns: CDC, Kafka, outbox pattern, event sourcing, CQRS
• Data modeling expertise: canonical models, dimensional modeling, schema evolution

AI & Semantic Data Competencies

• Hands-on experience with at least one vector database (Pinecone, Weaviate, Milvus, pgvector, Azure AI Search, or similar)
• RAG architecture understanding: embedding models, chunking strategies, retrieval optimization, hybrid search, reranking patterns
• Semantic modeling: designing data structures optimized for AI retrieval—knowledge representation, ontologies, domain-specific schemas
• Understanding of embedding pipelines: text preprocessing, embedding generation, vector indexing, incremental updates
• Familiarity with LLM context requirements and token constraints

AI-Native Engineering Practices

• 2+ years actively using AI coding assistants for database work with deep understanding of effective prompting for SQL and data engineering
• Experience building tools, scripts, or automation leveraging AI/LLM capabilities
• Familiarity with creating structured artifacts for AI consumption: documented schemas, annotated procedures, context files
• Vision for AI-assisted database engineering and ability to build enabling tooling

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
• Contributions to database tooling, AI/ML data infrastructure, or open-source projects
• Experience mentoring engineers or leading database/data architecture communities of practice

How We Work With You

Candidates apply through Get A Job.ai, where our talent team conducts initial screenings to understand your background and career goals. We then submit qualified candidates to our client for their review and interview process. Please do not contact the client directly—all communication flows through our recruiting team to ensure a smooth, professional experience for everyone involved.

Pay

Compensation details will be discussed during the screening process based on your experience and qualifications.

Equal Employment Opportunity

Get A Job.ai is committed to fair and equitable recruiting practices. We evaluate all candidates based on qualifications and merit, and we encourage applications from individuals of all backgrounds.