Note: The job is a remote job and is open to candidates in USA. WEX is seeking a Senior AI Data & Database Engineer to modernize critical SQL Server systems and build data infrastructure for AI applications and agents. The role focuses on database modernization, performance engineering, production-grade data pipelines, vector search and retrieval infrastructure, cloud provisioning, and collaboration with application, platform, and AI/ML engineering teams.
Responsibilities
- Analyze complex SQL Server stored procedures and identify embedded business logic, dependencies, and data access patterns
- Refactor stored procedures using established architectural patterns to simplify data access, improve maintainability, and enable business logic to move into services
- Design and execute database migrations while maintaining data integrity, availability, and backward compatibility
- Analyze execution plans, optimize queries, and design effective indexing strategies for high-volume workloads
- Implement event-driven database patterns including CDC, outbox patterns, and event publishing
- Build automated tests and validation processes for database changes, migrations, and refactored procedures
- Create structured, AI-consumable documentation including annotated schemas, procedures, dependencies, and system context
- Build embedding pipelines covering text extraction, preprocessing, chunking, embedding generation, and vector storage
- Implement and optimize vector databases, vector indexes, semantic search, and hybrid retrieval patterns
- Build synchronization pipelines that keep vector stores aligned with source systems
- Develop retrieval APIs and services consumed by AI applications and agents
- Implement evaluation and monitoring for RAG systems, including retrieval quality, latency, relevance, and data freshness
- Partner with AI/ML engineers to improve embedding strategies, retrieval quality, and overall AI data performance
- Design and implement reliable ETL/ELT pipelines across SQL Server, PostgreSQL, Snowflake, and cloud data services
- Build API-based ingestion, event streaming, and batch-processing workflows
- Implement data quality, validation, observability, and operational monitoring for data pipelines
- Develop infrastructure-as-code for database provisioning and configuration using Terraform and ARM/Bicep
- Support NoSQL data solutions including MongoDB and Cosmos DB, with a focus on data modeling and query performance
- Design data access patterns that support domain-driven architectures, including repositories, query services, and read models
- Use AI coding assistants daily to accelerate database analysis, code generation, debugging, testing, and documentation
- Develop prompts, scripts, and workflows that apply AI to database engineering and modernization challenges
- Contribute to AI-powered engineering tools such as stored procedure analyzers, schema documentation generators, and migration assistants
- Create structured context and artifacts that enable AI agents and coding tools to reason effectively about data systems
- Evaluate emerging AI tools and identify practical opportunities to improve engineering productivity
- Partner with application, platform, and AI/ML engineers to design scalable, reliable data solutions
- Participate in code and architecture reviews for database, data platform, and AI infrastructure changes
- Troubleshoot complex production data and performance issues and contribute to operational support as needed
- Document technical decisions, patterns, and solutions so knowledge is reusable across engineering teams
- Mentor engineers on database design, performance optimization, data engineering, and modern engineering practices
Skills
- Strong hands-on experience with **SQL Server, T-SQL, stored procedures, query optimization, and execution plans**
- Proven experience modernizing legacy database systems and decomposing complex database logic into maintainable application or service architectures
- Strong understanding of relational database design, indexing, transactions, data integrity, and performance engineering
- Experience building production-grade data pipelines and integrating data through APIs, events, and batch processes
- Experience with one or more modern data platforms such as **PostgreSQL, Snowflake, MongoDB, or Cosmos DB**
- Practical experience with **vector databases, embeddings, semantic search, RAG, or AI data pipelines**
- Understanding of event-driven architectures and patterns such as **CDC and transactional outbox**
- Experience with cloud platforms and infrastructure-as-code, preferably **AWS/Azure and Terraform**
- Strong software engineering fundamentals, including version control, automated testing, CI/CD, and code review practices
- Demonstrated ability to use AI coding assistants effectively and willingness to incorporate AI into day-to-day engineering work
- Experience building data infrastructure specifically for LLM or agentic applications
- Experience with vector databases such as **Pinecone, Azure AI Search, OpenSearch, pgvector, or similar technologies**
- Experience with Kafka or other event-streaming platforms
- Experience developing retrieval services or RAG evaluation frameworks
- Experience creating internal AI-powered developer tools or automation
- Experience working in large-scale, distributed, cloud-native environments
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
Company Overview
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