Wex Benefits
From Wex Benefits's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
Staff Data Platform Engineer, you will be the primary architect and visionary for the core data infrastructure that powers the entire enterprise.
Sr. Staff Data Platform Engineer
Position Summary
As a Sr. Staff Data Platform Engineer, you will be the primary architect and visionary for the core data infrastructure that powers the entire enterprise. This is a "platform-as-a-product" role; your mission is to build the internal foundation—creating the high-performance engines, abstraction layers, and self-service frameworks that enable hundreds of other engineers to move faster.
You will operate at the intersection of Systems Programming and Data Engineering, solving "hard-tech" problems like automated schema evolution, multi-engine compute optimization, and global data discovery. As a top-level technical individual contributor, you will bridge the gap between long-term business strategy and deep-kernel technical execution, ensuring WEX’s data platform remains a competitive advantage.
Is this role for you?
YES, if: You are a Backend/Software Engineer who loves solving "Big Data" problems, building APIs for data discovery, and optimizing distributed systems.
NO, if: Your primary expertise is writing SQL queries, building Tableau dashboards, or managing ETL workflows without deep experience in Java or Python system architecture.
Architectural Sovereignty: Define the 3-5 year technical roadmap for the Data Lakehouse. You aren't just using tools; you are deciding how storage, compute, and metadata layers (e.g., Apache Polaris, Unity Catalog or Datahub Catalog) interact at an elemental level.
Platform-as-a-Product: Build internal SDKs, CLI tools, and automated orchestration frameworks. Your goal is to abstract away cloud complexity via Control Planes and Custom Operators, allowing Data Engineers to focus on business logic rather than infrastructure boilerplate.
Internal R&D: Prototype and benchmark emerging technologies (e.g., specialized Spark extensions) to keep the platform at the bleeding edge of performance and cost-efficiency.
Global Governance & Security: Architect "compliance-by-design" systems. Automate data lineage, PII masking, and fine-grained access control across petabyte-scale environments without sacrificing developer velocity.
Engineering Excellence & Influence: Set the gold standard for code quality and system design across the company. You will lead Cross-Functional Architecture Reviews and serve as the final escalation point for the most complex system outages or performance bottlenecks.
Organizational Mentorship: Beyond individual mentoring, you will foster an "Engineering Community," influencing the hiring bar and professional development paths for the entire data engineering organization.
Experience: 15+ years in software engineering and distributed systems, with at least 4 years in a principal or staff-level capacity leading platform-scale initiatives.
Strong Software Foundations: Strong fundamentals on software engineering, system architecture, and scalable production applications (Algorithms, Data Structures, and System Design).
Experience in the Java/J2EE ecosystem (Spring Boot, Microservices) and python. We are looking for a developer who writes clean, testable, and high-performance code, not just scripts.
Data as a Product: Experience building the platforms / framework engines and APIs that power data movement, rather than just building the ETL/ELT pipelines themselves.
Deep internal knowledge of Apache Iceberg, Hudi, or Delta Lake (metadata management, manifest files, and compaction strategies).
Experience contributing to or deeply customizing open-source data projects (e.g., Spark, dbt).
Extensive experience with cloud architecture and services, including AWS (S3, EMR, Kubernetes, Lambda) and Azure.
Deep understanding of CI/CD automation, modern development tools, Git Actions, Terraform and frameworks.
AI Native Development: Experience leveraging AI Code Gen platforms into software development lifecycle (SDLC) to automate code generation, reviews, generate unit tests, and perform root-cause analysis of system failures.
LLM-Ops for Platform: Ability to architect the infrastructure required to support AI Agent development by enabling vector database integration.
Proven track record of "leading by influence"—driving adoption of new technologies across multiple autonomous teams.
Ability to communicate complex architectural trade-offs (e.g., "Latency vs. Consistency" or "Build vs. Buy") to C-suite executives and junior engineers alike.
Bachelor’s or Master’s degree in Computer Science (Distributed Systems focus) preferred, or equivalent deep industry experience.
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