Senior Database Architect

WEX Inc.; Bengaluru; On-site; INR 3,500,000 - 7,000,000; Full time

Job summary

WEX Inc. is seeking an experienced database engineer/architect to lead legacy database modernization in Bengaluru.

You will decompose large SQL Server procedures, migrate business logic to application services, and align schemas with domain-driven design across multiple platforms. You will design AI-ready data infrastructure, vector-based retrieval patterns, and event-driven data flows to support modern data platforms and enterprise-scale analytics.

Qualifications

• 8–12 years in database engineering and architecture, with enterprise-scale SQL Server experience.
• Deep SQL Server expertise: T-SQL optimization, stored procedure design and refactoring, query plan analysis, indexing strategies, and performance tuning.
• Hands-on modernization experience: decomposing complex stored procedures and migrating business logic to application services.
• Multi-platform data architecture: experience across SQL Server, PostgreSQL, NoSQL (MongoDB/Cosmos DB) and analytical platforms like Snowflake.
• Event-driven data patterns: CDC, Kafka, outbox pattern, event sourcing, CQRS—practical experience in production.

Responsibilities

• Analyze and decompose large SQL Server stored procedures with embedded business logic to create migration strategies.
• Design patterns to separate business rules from data access and move logic to application services.
• Lead refactoring aligning database structures with domain-driven design: bounded contexts, aggregates, domain events.
• Implement event-driven patterns to decouple systems from direct database dependencies.
• Optimize queries, indexing, and execution plans as part of modernization.
• Create migration playbooks and tooling for stored procedure modernization.
• Design semantic data models and vector DB solutions for AI retrieval and RAG implementations.
• Architect data solutions across SQL Server, PostgreSQL, MongoDB/Cosmos DB, Snowflake and vector DBs.
• Develop embedding pipelines and knowledge graphs for AI consumption.
• Promote data governance, data residency, and multi-region strategies.
• Mentor engineers on database design, semantic modeling, and AI data infra.

Job description

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

Success Looks Like

In 90 days: Completed assessment of priority stored procedure modernization targets and AI data infrastructure needs; delivered first AI-assisted analysis tooling; established vector database patterns for initial RAG implementations

In 6 months: Led decomposition of at least one major stored procedure system; semantic data models and RAG architecture patterns established and being adopted; AI-powered database engineering tools in active use by teams

In 12 months: Measurable reduction in stored procedure complexity across priority systems; AI data infrastructure supporting production agent workflows; recognized as the go-to expert for both database modernization and AI-native data architecture

Why This Role Matters

Data architecture is being transformed from two directions simultaneously. From the legacy side: business logic buried in stored procedures creates invisible dependencies that resist refactoring.

Traditional approaches to database modernization are slow, manual, and error-prone—AI can analyze thousands of lines of T-SQL, identify patterns, and accelerate migrations in ways that weren’t possible before.

From the AI side: agents and workflows need purpose-built data infrastructure. The semantic models, vector databases, and knowledge representations you design will determine how effectively AI can reason about our domains.

This isn’t a nice-to-have capability; it’s foundational to our AI-native engineering strategy. You’ll work at the intersection of these transformations—solving hard legacy problems while building the data infrastructure that makes AI-native applications possible.

The patterns you establish will shape how we approach data architecture across the enterprise.

WEX

WEX is a global commerce platform that helps businesses solve for operational complexities like employee benefits, managing and mobilizing fleets, and streamlining payments. With over 6,500 employees, we work with large and small companies in more than 200 countries and territories, and can tailor our services to meet the unique needs of their businesses.

We hire people who share our passion for continuous innovation and client service that is unparalleled in the industry.

Offering comprehensive and market competitive benefits, our offerings are designed to support your personal and professional well-being.

Equal Opportunity

WEX is an equal opportunity employer committed to diversity and inclusion in the workplace. All qualified applicants will receive consideration for employment without regard to sex, race, color, age, national origin, religion, sexual orientation, gender identity, protected veteran status, disability or other protected status.

WEX promotes a drug-free workplace. Qualified individuals with a disability have the right to request a reasonable accommodation. If you require a reasonable accommodation as a result of your disability at any point in the job application process, please submit your request through our Reasonable Accommodation Request Form. This form is for accommodation requests only and cannot be used to inquire about the status of applications.