Staff Technical Product Manager

Chewy•Bellevue, WA

19h

About The Position

Our Opportunity Are you passionate about defining the future of customer care through technology? Do you thrive in ambiguous, technically complex environments where customer experience, business strategy, data, and platform architecture intersect? If so, join Chewy’s Customer Care Product & Engineering (CCPE) team as a Staff Technical Product Manager and help shape the next generation of customer care. At Chewy CCPE, we deeply understand the experiences of both customers and customer care team members and build the products and platforms that power our industry-leading customer care experience. Our goal is to create intelligent, scalable, reliable, and seamless solutions that foster trust, improve efficiency, and enable long-term growth. As a Staff Technical Product Manager, you will set the product vision for a large customer care platform. You will bring clarity to ambiguous technical problem spaces, connect technical investments to customer and business outcomes, and anticipate the foundational capabilities needed to support future scale. You will work largely autonomously while partnering with Engineering, Architecture, Design, Data Science, Analytics, Operations, and business leaders. Through deep technical expertise and influential product leadership, you will shape roadmaps, architectural decisions, investment priorities, and operating mechanisms across organizational boundaries.

Requirements

Bachelor’s degree in Computer Science, Engineering, Mathematics, Statistics, Business, or another relevant technical or quantitative discipline, or equivalent practical experience.
8+ years of product management, technical product management, or comparable experience delivering technically complex products, platforms, or data-intensive systems.
Demonstrated success owning the strategy and roadmap for a substantial product, platform, or interconnected set of products with broad organizational impact.
Deep understanding of system architecture, APIs, databases, data structures, distributed systems, integrations, and performance tradeoffs.
Experience partnering with senior engineers, architects, data scientists, or machine learning experts to shape scalable technical solutions and influence architectural decisions.
Experience with AI, machine learning, automation, or data-intensive products, including familiarity with model performance, deployment considerations, monitoring, representative testing datasets, and failure modes.
Strong systems thinking, with the ability to connect decisions across upstream and downstream systems, identify platform-level risks, and balance immediate delivery with foundational investment.
Demonstrated ability to lead through influence across organizational boundaries and build alignment among senior technical, business, and executive stakeholders.
Proven ability to structure ambiguous, high-risk problem spaces and create decision frameworks where requirements, ownership, or technical direction are not yet defined.
Strong analytical capabilities, including experience analyzing large datasets, defining product and technical KPIs, designing and interpreting experiments, and translating evidence into product decisions.
Experience defining robust testing strategies, including automated testing, manual testing, user acceptance testing, and representative or golden datasets for AI and ML solutions.
Exceptional written and verbal communication skills, including the ability to explain complex models, algorithms, system architectures, and technical tradeoffs to both technical and non-technical audiences.
Experience developing clear technical product documentation, including product strategies, roadmaps, system requirements, API specifications, model inputs and outputs, data formats, performance expectations, edge cases, testing plans, KPIs, and failure modes.
A record of mentoring product managers, advancing product-management practices, or serving as a subject-matter expert in a technically complex domain.

Responsibilities

Set the vision for a large system or platform: Define and champion the long-term product vision for a substantial customer care product with an interconnected set of sytems. Bring clarity to ambiguous technical opportunities and ensure the strategy supports evolving customer, agent, business, and platform needs.
Own product strategy and roadmaps: Lead the development and authorship of your area’s product strategy, including its contribution to Chewy’s three-year planning. Build and own annual and multi-quarter roadmaps that connect technical investments to broader portfolio and enterprise priorities.
Make high-leverage product investments: Identify opportunities where shared platforms, foundational capabilities, AI, automation, or data products can unlock value across multiple teams. Develop bespoke prioritization frameworks and make explicit tradeoffs among customer value, business impact, scalability, technical debt, risk, and delivery constraints.
Anticipate scale and platform needs: Proactively identify future capacity, performance, reliability, security, observability, data-quality, and extensibility requirements. Scope foundational work before immediate demand emerges and ensure near-term decisions support long-term platform success.
Shape technical direction across domains: Partner with senior engineers, architects, data scientists, and machine learning experts to establish technical direction across interconnected domains. Influence architecture and design patterns that align with product strategy, system performance, and long-term maintainability.
Improve platform coherence: Identify opportunities to consolidate systems, reduce unnecessary complexity, improve reuse, and strengthen shared capabilities. Guide build-versus-buy decisions and contribute to organizational standards for platform selection, integration, monitoring, and support.
Guide architectural tradeoffs: Make product-scope decisions informed by system architecture, technical dependencies, technical debt, and organizational risk. Thread architectural decisions across upstream and downstream systems and ensure teams understand their broader platform implications.
Lead customer-centric innovation: Guide teams toward customer- and agent-centered innovation across technical domains. Use evolving customer behavior, operational insights, market trends, and system data to identify opportunities for personalization, intelligent automation, and data-driven improvement.
Connect technology to business results: Establish a clear connection between technical investments and outcomes such as customer satisfaction, agent effectiveness, resolution quality, operational efficiency, reliability, and business growth. Define product, platform, and model-health metrics that demonstrate realized value.
Advance AI- and ML-enabled customer care: Identify, evaluate, and scale AI, machine learning, and automation capabilities that improve customer interactions and agent workflows. Partner with technical teams to define model inputs and outputs, performance requirements, failure modes, monitoring, representative test data, and human oversight.
Align stakeholders across the organization: Serve as connective tissue between business and technical teams. Identify and engage the right stakeholders, manage competing priorities, balance departmental objectives with broader organizational goals, and build alignment around platform-level investments.
Influence senior and executive leaders: Communicate crisp, data-driven product narratives to audiences ranging from technical teams to executive leadership. Anticipate questions, welcome productive debate, surface tradeoffs, and influence investment in shared systems and technical foundations.