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# Sr Staff Software Engineer- Data Platform

- **Company:** WEX Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $220,000 - $255,800
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Amazon S3, Apache HTTP Server, Business Logic, Unit Testing, Microsoft Azure, Mobile Application Development, Cloud Computing, Cloud Engineering, Code Generation, Software Quality, Databases, Continuous Integration, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Data Transformation, Data Structures, Programming Tools, Distributed Systems, Fault Tolerance, Python (Programming Language), Meta-Data Management, Systems Development Life Cycle, Software Engineering, Systems Architecture, Large Language Models, Snowflake, Apache Spark, Spring-boot, Electronic Medical Records, Agentic-AI, Git, Data Lakes, Kubernetes, Information Technology, Data Lineage, Low Latency, Real Time Data, Data Lakehouse, Functional Programming, Terraform, Data Pipelines, Databricks, Microservices
- **Published:** November 12, 2026
- **Apply:** https://wexinc.wd5.myworkdayjobs.com/WEXInc/job/US---Remote/Sr-Staff---Data-Platform-Engineer_R20514

## About the Role

* 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.

Core Technical Competencies (Software Engineering Focus):

 * 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.

Data Lakehouse Mastery:

 * 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 designing and building high-throughput, fault-tolerant data pipelines and orchestration frameworks that ensure robust data transformations and high data quality.

Cloud & Infrastructure:

 * 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-Driven Development & Productivity:

 * 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.

Leadership & Vision:

 * 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.

Education:

 * Bachelor's or Master's degree in Computer Science (Distributed Systems focus) preferred, or equivalent deep industry experience.

## Description

* 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 Iceberg, Snowflake Horizon or Databricks Unity 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.
 * AI Governance Lake: Facilitate real-time data ingestion by implementing streaming support for Open Telemetry data from AI Agents into the Data Lake, drive the development of advanced AI evaluation metrics and reporting.
 * 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.