Engineering Manager, Insights

Decagon•San Francisco, CA

1d•Onsite

About The Position

Decagon is the leading conversational AI platform empowering every brand to deliver concierge customer experience. Our AI agents provide intelligent, human-like responses across chat, email, and voice, resolving millions of customer inquiries across every language and at any time. Since coming out of stealth, Decagon has experienced rapid growth. We partner with industry leaders like Hertz, Eventbrite, Duolingo, Oura, Bilt, Curology, and Samsara to redefine customer experience at scale. We've raised over $200M from Bain Capital Ventures, Accel, a16z, BOND Capital, A, Elad Gil, and notable angels such as the founders of Box, Airtable, Rippling, Okta, Lattice, and Klaviyo. We’re an in-office company, driven by a shared commitment to excellence and velocity. Our values—customers are everything, relentless momentum, winner’s mindset, and stronger together—shape how we work and grow as a team. The Insights team builds the product surfaces that help customers understand what is happening in their agent conversations and improve agent quality over time. We turn large volumes of unstructured conversation data into clear explanations, intuitive workflows, and actionable next steps. Our work spans three core areas: Visibility and reporting: help teams track performance, trends, and drivers of customer outcomes across channels. Proactive quality and risk detection: continuously surface issues like emerging failure modes, regressions, or policy and compliance risks, so teams can respond before they impact customers. Actionable recommendations: guide users toward concrete improvements, including suggested updates to agent instructions, knowledge, and workflows based on real conversation patterns. We own and scale a set of analytics and quality products today, and we are building new ones that deepen how customers learn from their data, diagnose issues, and iterate on agent behavior. This is a product focused, technical leadership role responsible for scaling existing analytics experiences and building new 0 to 1 products that help customers learn from their data and take action quickly. You will partner closely with Product, Design, Customer Success, Data Science, and Agent Engineering to identify customer needs, propose new product directions, and ship iteratively based on real usage. Success requires strong people leadership, crisp execution in ambiguous spaces, and the technical judgment to set architectural direction across full-stack product surfaces and the data systems behind them.

Requirements

Have 3+ years of engineering management experience, or equivalent experience leading teams through delivery, coaching, and stakeholder management.
Have a strong 0 to 1 product development sense. You can turn ambiguity into a clear learning plan, ship quickly, and iterate toward product-market fit.
Have led large, long lived codebases and can set architectural direction while keeping engineering quality high as systems evolve.
Are comfortable staying technical as a manager. You can guide design reviews, dive into complex issues, and lead incident and post-incident learning without needing to be the primary implementer.
Have built user facing analytics or insight products where correctness, trust, and explainability matter.
Communicate clearly and collaborate well across Product, Design, Customer Success, and technical partner teams.
Invest in people. You coach engineers, raise the bar for execution, and build a culture of ownership and craft.

Nice To Haves

Experience building analytics, monitoring, QA, or insight products.
Experience working with agent driven or LLM powered systems.
Experience designing scalable data models across relational and analytical databases.
Experience building high-volume data pipelines, streaming systems, or near real-time analytics.

Responsibilities

Build, lead, and develop a high performing team, including hiring, coaching, and performance management.
Own the engineering strategy, roadmap, and execution, balancing iteration speed with correctness, trust, and scalability.
Partner closely with Product and Customer Success to understand customer needs, propose new product directions, and ship iteratively based on real usage.
Drive 0 to 1 development through rapid prototyping, experimentation, and iterative deployment, then scale what works.
Set architectural direction across user facing experiences and the underlying data models, pipelines, and APIs that power reporting, detection, and insight generation.
Establish standards for data quality, metric integrity, observability, and debuggability so we can diagnose issues quickly and prevent repeat incidents.
Collaborate with Data Science, Research, and Agent Engineering to connect conversation signals, evaluations, and customer outcomes into actionable product experiences.