AI Data & Database Engineer - Modernization & AI Infrastructure

WEX, Inc.

United States

On-site

USD 140,000 - 220,000

Full time

10 days ago

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Benefits offered by this job

Health Insurance

Retirement Plan

Paid Time Off

Tuition Reimbursement

Flexible Spending Accounts

Life Insurance

Job summary

WEX, Inc. is seeking a Senior AI Data & Database Engineer to modernize SQL Server systems and build AI-ready data infrastructure. You will work on data pipelines, vector search, and retrieval services, partnering with AI/ML teams to improve performance and scalability.

The role spans database modernization, performance engineering, and AI-native data platforms, using tools like Copilot and Claude Code to automate migrations and validate changes.

Qualifications

Proven hands-on experience modernizing SQL Server systems and building data pipelines.
Ability to decompose legacy logic and implement AI-ready data architectures.
Strong understanding of vector stores, semantic search, and retrieval patterns.

Responsibilities

Analyze and refactor complex stored procedures to improve performance and maintainability.
Design migrations with data integrity, backward compatibility, and validation tests.
Build embedding pipelines, vector storage, and retrieval services for AI apps.
Develop and optimize ETL/ELT pipelines across SQL Server, PostgreSQL, Snowflake.
Collaborate with AI/ML teams to enhance embedding strategies and retrieval quality.

Skills

SQL Server

T-SQL

Stored Procedures

Query Optimization

Execution Plans

Data Pipelines

Vector Databases

Embeddings

RAG

AI Pipelines

AWS/Azure

Terraform

CI/CD

Code Review

Tools

GitHub Copilot

Cursor

Claude Code

Job description

WEX is looking for a Senior AI Data & Database Engineer to help modernize critical SQL Server systems while building the data infrastructure that powers AI applications and agents. This is a hands‑on engineering role spanning database modernization, performance engineering, data pipelines, and AI-native retrieval infrastructure.

You’ll work across two highly connected areas: modernizing complex legacy database systems by extracting business logic, improving performance, and enabling event-driven architectures; and building AI‑ready data capabilities including embedding pipelines, vector search, RAG infrastructure, and retrieval services.

We’re looking for someone who enjoys solving hard data problems and is excited to use AI as an engineering accelerator. You’ll use tools such as GitHub Copilot, Cursor, and Claude Code to analyze legacy code, generate and validate migration scripts, troubleshoot performance issues, and automate database engineering workflows.

What You’ll Do

Database Modernization & Performance

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.

AI‑Native Data Infrastructure

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.

Data Platform Engineering

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.

AI‑Assisted Engineering

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.

Collaboration & Engineering Excellence

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.

What You’ll Bring

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.

Preferred Qualifications

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.

How You’ll Make an Impact

You’ll play a key role in transforming how WEX manages and uses data—from modernizing foundational SQL Server systems to building the retrieval and data infrastructure required for the next generation of AI applications. This role is ideal for an engineer who enjoys deep technical problem‑solving and wants to work at the intersection of database engineering, modernisation, and AI infrastructure.

The base pay range represents the anticipated low and high end of the pay range for this position. Actual pay rates will vary and will be based on various factors, such as your qualifications, skills, competencies, and proficiency for the role. Base pay is one component of WEX’s total compensation package. Most sales positions are eligible for commission under the terms of an applicable plan. Non‑sales roles are typically eligible for a quarterly or annual bonus based on their role and applicable plan.

WEX’s comprehensive and market‑competitive benefits are designed to support your personal and professional well‑being. Benefits include