Sr Manager Software Engineering - Data Acquisition

WEX•Boston, MA

2d•$175,600 - $204,300•Remote

About The Position

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.

Requirements

Bachelor's or Master's degree in Computer Science, Engineering, or a related technical field.
10+ years of experience in software engineering, with at least 5+ years in a management role overseeing software engineering or data acquisition teams.
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.).
Proven ability to lead, motivate, and manage multiple distributed teams.
Excellent communication, presentation, and interpersonal skills.
Strong analytical and problem-solving skills, with the ability to define solutions for complex technical challenges.

Nice To Haves

Experience in leading virtual teams is highly desirable

Responsibilities

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.
Pioneer the adoption of AI-assisted software development across engineering teams. Define metrics and implement AI-enabled development workflows to measurably enhance engineering productivity.
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.
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.
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.
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.
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.
Institute and champion continuous improvement in engineering processes, tools, and methodologies, including CI/CD, automation, monitoring, and alerting practices.
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.
Oversee budget allocation, resource management, and capacity planning to ensure the strategic growth of the data acquisition organization.