# Manager, Product Software Engineering at Wolters Kluwer

Wolters Kluwer is hiring a Manager, Product Software Engineering. It pays $116k-$204k a year and Level rates it Works on AI ●●●○; you can [apply on Wolters Kluwer](https://jobsbylevel.com/go/ab4b5cf5-3cee-4bf7-9765-6583e2ba0f55?utm_medium=ai-agent).

AI Level 3, AI centrality 66 out of 100. USA - Coppell, TX.

## Details

- Company: [Wolters Kluwer](https://jobsbylevel.com/companies/wolters-kluwer)
- AI level: AI Level 3 (score 66 out of 100)
- Location: USA - Coppell, TX
- Salary: $116k-$204k
- Posted: November 9, 2026
- Apply: https://jobsbylevel.com/go/ab4b5cf5-3cee-4bf7-9765-6583e2ba0f55?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=manager-product-software-engineering-at-wolters-kluwer-baad43

## Prepare for this job

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## Description

Role Summary The Senior Manager, Product Software Engineering is responsible for leading and scaling high-performing engineering teams while driving execution, technical excellence, organizational effectiveness, and engineering innovation across multiple product initiatives. This role serves as a key extension of engineering leadership, enabling the organization to scale delivery, improve engineering productivity, strengthen technical quality, and accelerate modernization initiatives. The individual will partner closely with Product Management, Architecture, UX, QA, DevOps, Business Leaders, and Engineering Managers to execute strategic roadmaps, mature engineering practices, and build a culture of accountability, continuous learning, and AI-driven software development . The ideal candidate combines strong people leadership, technical depth, delivery management expertise, and a passion for leveraging modern engineering capabilities including AI-assisted development, automation, developer productivity tooling, and Spec-Driven Development practices. Key Responsibilities Lead and develop multiple engineering teams across products, platforms, and technology initiatives. Foster a high-performance engineering culture focused on ownership, innovation, collaboration, and continuous improvement. Create an environment that encourages experimentation, learning, and adoption of emerging technologies. Own execution of engineering roadmaps and strategic initiatives across multiple teams. Drive predictable delivery by implementing planning disciplines, dependency management, risk mitigation, and outcome-based tracking. Establish metrics and operational reviews for delivery quality, velocity, productivity, reliability, and customer impact. Partner with Product and Business stakeholders to prioritize initiatives and balance strategic investments with operational commitments. Ensure engineering teams consistently deliver secure, scalable, maintainable, and high-quality solutions. Partner with Product Architects and Technical Leads to guide engineering solutions aligned with enterprise architecture standards. Support modernization initiatives involving cloud-native architectures, APIs, platforms, data services, AI capabilities, and distributed systems. Influence technical strategy, platform investments, and engineering standards across teams. Promote engineering best practices in software design, architecture, testing, observability, security, resiliency, and maintainability. Champion automation across development, testing, deployment, operations, and support processes. Ensure technical debt management and platform sustainability remain part of engineering planning. Partner closely with Product Management, Architecture, UX, QA, Security, DevOps, and Business stakeholders. Translate business objectives into executable engineering plans and measurable outcomes. Drive alignment across geographically distributed teams and organizational boundaries. Influence decisions at leadership levels through data-driven recommendations and clear communication. AI-Driven Engineering & Developer Productivity Drive adoption of AI-first software engineering practices across the SDLC. Champion AI-assisted development tools including GitHub Copilot, agentic development platforms, automated code reviews, AI-powered testing, and engineering intelligence solutions. Promote Spec-Driven Development methodologies and AI-Oriented SDLC practices to improve delivery predictability and quality. Leverage engineering metrics, analytics, and AI insights to optimize productivity, code quality, system performance, and operational efficiency. Evaluate emerging AI technologies and identify opportunities to accelerate product development and engineering effectiveness. Required Skills & Experience Engineering & Technology Extensive experience leading software engineering organizations delivering enterprise-scale products and platforms. Bachelor’s degree in Computer Science, Software

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