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; Dallas, TX; San Francisco Bay Area, CA; and Seattle/WA.
About the Team/Role
As WEX continues to scale its Data-as-a-Service (DaaS) platform, the Data Acquisition Team plays a critical role in enabling secure, scalable, and reliable ingestion of data from hundreds of internal systems and external sources.
We are seeking a hands-on Senior Manager, Software Engineering - Data Acquisition to lead our team in acquiring and processing high-volume data, while simultaneously driving the evolution toward AI-augmented, spec-driven software development to enhance platform scalability and delivery speed.
This role requires a strong leader with deep technical expertise in data pipelines, distributed systems, and cloud architecture, who can drive technical excellence, foster a culture of innovation, and align the data acquisition strategy with overall business goals.
Responsibilities:
Engineering Leadership & Team Development: Recruit, mentor, and lead a high-performing team of software engineers focused on data acquisition, fostering a collaborative and inclusive culture. Oversee performance management, career pathing, and top-tier talent acquisition.
AI-Augmented Development Strategy: Pioneer the adoption of AI-assisted software development across engineering teams. Define metrics and implement AI-enabled development workflows to measurably enhance engineering productivity.
Specification-Driven Development (SDD): Establish and enforce a specification-first development methodology. Standardize templates for all key artifacts (APIs, data contracts, ingestion pipelines, architecture) and ensure end-to-end traceability across implementation, validation, deployment, and observability.
Architectural Transformation & Modernization: Drive the migration to automated, metadata-driven, and declarative engineering architectures. Develop reusable frameworks that translate technical specifications directly into generated code, deployment artifacts, and operational controls.
Strategic Roadmap Execution: Define and execute the technical roadmap for all data acquisition pipelines and systems, ensuring the infrastructure is highly scalable, reliable, secure, and cost-effective to accommodate accelerating data volume and velocity.
Technical Governance & Oversight: Provide authoritative technical direction on the design, development, and maintenance of mission-critical data ingestion frameworks. Mandate and enforce best practices for software engineering, data governance, and data quality.
Stakeholder Collaboration: Partner closely with Product Management, Data Science, Data Governance, and other engineering teams to align data solutions with overarching business requirements and strategic data needs.
Engineering Process Optimization: Institute and champion continuous improvement in engineering processes, tools, and methodologies, including CI/CD, automation, monitoring, and alerting practices.
Sustained Operational Excellence: Guarantee the high availability and performance of all data acquisition systems, taking ownership of incident response, recovery, and thorough root cause analysis for major service disruptions.
Resource & Financial Stewardship: Oversee budget allocation, resource management, and capacity planning to ensure the strategic growth of the data acquisition organization.
Qualifications:
Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.
Experience: 10+ years of experience in software engineering, with at least 5+ years in a management role overseeing software engineering or data acquisition teams.
Experience in leading virtual teams is highly desirable
Technical Expertise:
Experience implementing AI-assisted engineering workflows in production software organizations.
Deep understanding of specification-driven engineering, declarative system design, or model-driven development.
Deep expertise in building and managing high-volume, real-time and batch data pipelines (e.g., Kafka, Kinesis, Pulsar).
Proficiency with cloud platforms (e.g., AWS, Azure, GCP) and experience designing scalable, serverless, or containerized data ingestion architectures (e.g., Kubernetes, EKS/AKS/GKE).
Strong knowledge of various data sources, integration patterns (APIs, web scraping, messaging queues), and ETL/ELT tools.
Expertise in programming languages such as Java, Python, Scala, or Go.
Solid understanding of database technologies (SQL, NoSQL, Data Warehouses like Snowflake, Redshift, etc.).
Leadership Skills: Proven ability to lead, motivate, and manage multiple distributed teams. Excellent communication, presentation, and interpersonal skills.
Problem Solving: Strong analytical and problem-solving skills, with the ability to define solutions for complex technical challenges.
