SDE 3
First seen by Alion on Nov 13, 2026. WEX scores A on the Alion truth index.
Senior Software Engineer, Semantic Data Team
About the team / Role
WEX is reimagining its enterprise data platform with a powerful goal: transforming raw data into
semantically meaningful, reusable, and trusted business assets. As a Senior Software Engineer
on the Semantic Data Team, you'll contribute to designing, building, and maintaining control
plane for Semantic layer and AI Native data platforms supporting our core 360 data objects
(Customer, Fleet, Health and Payments)
An embedded DataOps backbone and self-service control plane ensures governance, lineage,
observability, and operational efficiency at scale. Together, these elements empower WEX to
modernize responsibly—continuously delivering business value while evolving toward a future
where intelligence and automation are built on a foundation of governed, high-quality data.
This team is at the heart of WEX's DaaS platform where the platform integrates DataOps
automation, control plane services, and governance tooling to uphold quality and trust at scale,
enabling trusted, production-grade semantic models through automated controls, audit
validation, and operational alignment.
Wex is highly invested in building AI native data platform. The Agentic Control Plane is the
runtime layer that determines how an AI agent interprets a user request, assembles the right
context, applies the right rules and constraints, uses approved tools, and drives the next step.
We're looking for an AI-native engineer: someone who builds with modern AI coding tools
(Claude, Copilot, Cursor, and similar) and Spec-Driven Development (SDD) as a core part of
their daily workflow. You'll use these tools to accelerate development and apply solid
engineering judgment to ship production-grade, trustworthy data assets.
How you'll make an impact
● 5–8 years of experience in data engineering or software engineering with a focus on
data transformation, modeling, or analytics platforms.
● Strong proficiency in SQL and at least one general-purpose language such as Python or
Scala.
● Demonstrated experience as an AI-native engineer—using tools like Claude, GitHub
Copilot, Cursor, or similar as a regular part of your development workflow.
● AI-Native Architecture: You are passionate about the future of data intelligence and have
hands-on experience with LLM-driven architectures. You are comfortable designing RAG
systems, building agentic workflows (e.g., LangGraph, CrewAI), and working with
vector/graph databases to manage context and ontology.
● Familiarity with modern AI engineering practices such as prompt design, Spec-Driven
Development (SDD), and AI-assisted code review.
● Advanced Data Orchestration & Pipeline Reliability: You possess deep expertise in
building and managing complex orchestration systems (specifically Airflow) and are
obsessed with reliability. You know how to engineer quality-first pipelines using
frameworks like Great Expectations to ensure data trust at scale.
● Infrastructure as Code (IaC) & Platform Engineering: You are fluent in the modern
DevOps stack—including Terraform, Kubernetes, and CI/CD automation—allowing you
to build self-healing, scalable infrastructure that minimizes operational toil.
● Enterprise Governance & Metadata Management: You have a proven track record of
implementing automated governance, specifically utilizing DataHub or similar platforms
to manage end-to-end lineage, cataloging, and RBAC policies that make security
inherent to the data object.
● Observability & Incident Management: You understand that the platform is the product.
You bring experience building comprehensive observability frameworks (e.g., Grafana,
xMatters integration) that move teams from reactive firefighting to proactive, automated
alerting.
● Self-Service Mindset: You advocate for developer autonomy over manual intervention.
You have experience building intuitive portals and developer tools that enable domain
teams to build, manage, and consume trusted data products independently.
● Solid understanding of data quality practices—including validation, enrichment, schema
enforcement, and business rule encoding.
● Comfort operating in a collaborative, cross-functional environment, balancing business
logic with platform scalability.
● A proven track record for traceability, reproducibility, and semantic clarity—you build data
models others can trust and reuse.