Product Manager, Data Team

Regal•New York, NY

1d•Hybrid

About The Position

As a Product Manager on the Data team, you'll own one of the most important surfaces in our product: how customers see, trust, and act on what their AI agents are doing. You'll partner closely with engineering and design to transform how observability works at Regal building reporting, analytics, and insight experiences that make complex data feel intuitive and actionable. When customers can see exactly how their agents are performing and why, they expand use cases, trust the platform more, and grow their adoption of Voice AI. That's the outcome this role is built to deliver. You'll go deep with customers to understand the decisions they're trying to make, then translate those insights into crisp product bets with our data engineering team. If you're a data-native PM (or an analyst ready to make the jump) who loves bridging the gap between messy data and elegant customer experiences, this role is for you.

Requirements

Deeply Data-Literate: 2 4 years of experience as a Product Manager, ideally with at least some of that time on a data, analytics, or reporting product. Strong SQL proficiency is required. Experience as a data analyst before transitioning into PM is ideal. You're comfortable in the weeds of a dataset and understand experiment design and statistical reasoning well enough to push back on flawed analyses.
Customer-Obsessed: You can point to examples of bridging complex, technical subject matter and what customers actually need. You design for the real decision the customer is trying to make not the dashboard you happen to have.
Proven Shipper: You've driven multiple initiatives end to end, from idea through launch, and can speak credibly to the impact and the tradeoffs. You've partnered closely with design and shipped polished, customer-facing product experiences.
Entrepreneurial & High Agency: You operate with strong conviction, lead initiatives with limited hand-holding, and move fast under ambiguity. You act with urgency and ownership.
Deeply Curious: You love to learn new things and go very deep on any problem you're working on whether that's a customer workflow, a dataset, or a new analytics pattern.
Strong Communicator & Collaborator: You can take foggy product initiatives or customer problems and bring clarity to what the most important problem is and what should happen next. You work effectively across engineering, design, customers, and executives.
Creative Problem Solver: You develop unique solutions to the problems that face you and work well with constrained resources.
High Bar: You hold a high standard for your work and the work of others, aiming to ship the highest quality products to our customers.

Nice To Haves

Early-stage (pre-Series C) startup experience preferred but not required.

Responsibilities

Own the Data Product Roadmap: Partner with our data engineering team to own the roadmap and key success metrics for how customers access, understand, and act on Regal's data from reporting and analytics surfaces to the underlying datasets, dashboards, and insights.
Transform Observability: Reimagine how customers understand what their AI agents are doing. Make it more intuitive, simpler, and trustworthy so customers expand to more use cases and grow their AI minutes with confidence.
Develop Deep Customer Understanding: Go deep with customers to learn how they use Regal's data today, where they get stuck, and what decisions they're trying to make. Bring those insights back as product bets the team can rally around.
Ship Polished Data Experiences: Partner with design to ship data experiences that feel accessible, trustworthy, and actionable not just another dashboard.
Bridge Raw Data and Customer Value: Translate complex datasets into reporting, analytics, and insight experiences that customers can actually use to run their business and make better decisions.
Define the Measurement Framework: Drive the metrics, instrumentation, and experimentation framework that the broader company uses to measure product impact raising data literacy and self-serve across GTM, CS, and Product.
Lead Initiatives End to End: Take initiatives from customer discovery and problem framing through scoping, launch, measurement, and iteration moving fast and shipping meaningful improvements on a weekly cadence.