About the Team/Role
We’re building the semantic backbone of WEX’s Data-as-a-Service (DaaS) platform—an extensible data layer that turns raw data into trusted, reusable, and business-aligned assets. As a Staff Software Engineer on the Semantic Data Modeling team, you will be at the forefront of designing and scaling the data foundation that supports analytics, AI, and operational decisions across all WEX domains.
This isn’t a data wrangling or dashboard-building role. It’s a deeply technical, engineering-first position focused on building scalable systems that model complex business entities and relationships through high-performance, semantically meaningful data assets.
WEX is re-architecting its data platform to enable domain ownership, semantic clarity, and enterprise-wide reuse. This role is critical to shaping that foundation—empowering teams to build once and use everywhere, from dashboards to machine learning pipelines.
If you’re passionate about building systems that scale, and want to design the foundation that defines meaning in data, we’d love to hear from you.
How you’ll make an impact
- Architect and implement large-scale, semantically rich data objects (e.g., Customer 360, Fleet 360) that serve as the single source of truth across domains.
- Design for scale, reliability, and performance, handling billions of records and thousands of attributes across sources.
- Build modular, testable, and versioned transformation pipelines with a strong focus on readability, maintainability, and long-term scalability.
- Solve for entity resolution, time-aware modeling, and multi-domain relationships using first-principles thinking and clean abstractions.
- Collaborate with architects, product managers, and domain leads to translate complex business logic into scalable data design patterns.
- Lead design reviews, code quality standards, and performance benchmarking within the semantic modeling engineering team.
Experience you’ll bring:
- 8+ years of experience in software engineering or data-intensive system design, ideally in environments managing complex, high-volume data ecosystems.
- A systems thinking mindset—you consider data as a platform, not a pipeline.
- Strong understanding of data modeling principles, business semantics, and the challenges of modeling real-world entities at scale.
- Proven experience building and optimizing scalable, distributed systems, including schema design, storage strategy, and lifecycle management.
- Deep focus on code quality, testing, documentation, and version control as foundational engineering practices.
- Ability to thrive in a highly collaborative environment, working across product, engineering, and business functions.
