This is a remote position; however, the candidate must reside within 30 miles of one of the following locations: Portland, ME; Boston, MA; Chicago, IL; San Francisco Bay Area, CA; and Seattle/WA.
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 Staff Software Engineer on the Semantic Data Lake Team, you'll play a critical role in designing, building, and maintaining our core 360 data objects—such as Customer360, Fleet360, and Provider360.
These wide, entity-based tables are foundational to our analytics, AI, and product platforms. You'll implement rich transformation logic, encode business rules, and ensure data consistency across domains, making our data models both technically scalable and business-ready.
This team is at the heart of WEX's DaaS platform—bridging raw data with meaningful business insights. You'll help define and deliver the semantic backbone of our products, analytics, and machine learning systems.
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, not an occasional add-on. You'll use these tools to accelerate design, generate and refactor transformation logic, write tests, document semantics, and explore data—while applying the engineering judgment needed to ship production-grade, trustworthy data assets.
If you're excited about building semantic models that carry real-world meaning, scale to billions of records, and unify how a business understands its world—and doing it with the leverage of modern AI tooling—this is your next big move.
How you'll make an impactDesign and implement semantically consistent, scalable 360 data models that integrate data across domains.
Build and maintain transformation pipelines that apply cleansing, standardization, enrichment, and derived logic to domain datasets.
Write production-quality, testable code in SQL and Python (or equivalent)—delivering performant and maintainable data assets.
Leverage AI coding assistants (Claude, Copilot, Cursor, and similar) to accelerate development—drafting transformation logic, generating tests, refactoring pipelines, exploring datasets, and producing semantic documentation—while critically reviewing AI output for correctness, performance, and alignment with business rules.
Develop and share patterns, prompts, and workflows that help the team get more leverage out of AI tooling, raising the bar for AI-native engineering practices across the Semantic Data Team.
Work closely with domain experts, data scientists, and product stakeholders to translate business concepts into interpretable, decision-ready data models.
Implement logic for classifications, KPIs, scoring algorithms, and business rules, ensuring traceability and data lineage.
Help define and enforce standards for data modeling, documentation, and governance within the semantic layer—including standards for responsible, auditable use of AI-generated code and artifacts.
Collaborate across teams to integrate with ingestion, MDM, and data product layers, and explore opportunities to expose 360 objects to LLM-powered and agentic applications.
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 part of your everyday development workflow, with a clear point of view on where they accelerate your work and where human judgment is essential.
Comfort with modern AI engineering practices such as prompt design, context engineering, Spec-Driven Development (SDD), AI-assisted code review, and integrating LLMs or AI agents into engineering or data workflows.
Experience building and scaling wide, entity-based tables and modeling domain concepts (e.g., customer, fleet, provider) into durable data objects.
Solid understanding of data quality practices—including validation, enrichment, schema enforcement, and business rule encoding.
Experience working with large-scale datasets and optimizing transformation pipelines for performance and maintainability.
Comfort operating in a collaborative, cross-functional environment, balancing business logic with platform scalability.
A mindset for traceability, reproducibility, and semantic clarity—you build data models others (humans and AI systems alike) can trust and reuse.
Bachelor's degree in Computer Science, Software Engineering, or related field. A Master's or PhD in Data Science, Machine Learning, Artificial Intelligence, Computer Science, or Statistics is a big plus.
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