Senior Software Engineer - Semantic Data Lake

WEX is reimagining its enterprise data platform, and they are seeking a Senior Software Engineer on the Semantic Data Lake Team to design, build, and maintain core data objects. The role involves implementing transformation logic, ensuring data consistency, and leveraging AI coding tools to enhance development processes.

Responsibilities:

• Design 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
• Partner closely with the data products team to understand business requirements and ensure semantic models align with their needs
• Implement logic for classifications, KPIs, scoring algorithms, and business rules, ensuring traceability and data lineage
• 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

Requirements:

• 4-8 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, 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