Technical Product Manager

1 month from now
US > Philadelphia
Data Engineering

Job Description

Role Highlights

Product Ownership

  • Own end-to-end product management for BigQuery Connectors, Looker dashboards, and AI Agents, from requirements through QA, documentation, and ongoing health monitoring

  • Define product specs and acceptance criteria in partnership with division leaders and SMEs

  • Monitor product health, usage metrics, and production incidents; address issues with urgency

  • Make recommendations on what to productize, sunset, maintain, or optimize

** Sprint Planning & Engineering Coordination**

  • Own the sprint planning process end-to-end, in partnership with a Project Manager

  • Oversee data engineer hours and priorities across sprint cycles

  • Own backlog prioritization, capacity planning, and sprint completion accountability

  • Balance competing priorities across new development, maintenance, and custom client work

** Productization Execution**

  • Execute the productization of validated R&D innovations in partnership with the Director of Engineering & Product

  • Enable Engineering to transform prototypes into production-ready products

  • Create maintenance plans and support models; partner with the Innovation team on handoff requirements

** Cross-Functional Coordination**

  •  Own cross-functional relationships with R&D, division leads, Marketing, and AI Strategy

  •  Serve as the primary point of contact for data product questions and timelines

  •  Translate technical constraints and capacity realities into clear stakeholder communication

** Data Governance & Platform Administration**

  • Manage data access and permissions across Looker and data platforms

  • Own platform integrations, system administration, and compliance with data security and client confidentiality requirements

  • Maintain monitoring dashboards for system health and usage

** Team Management**

  • Manage Product Managers and conduct performance reviews and career development conversations

  • Build clear roles, responsibilities, and success criteria as the team scales

  • Develop product management capability in direct reports

You'll be a good fit if....

  • You have hands-on product management experience, including writing specs, testing features, and maintaining documentation, not just overseeing others who do it

  • You have worked with data-intensive products and are comfortable discussing pipelines, APIs, and architecture with engineers

  • You have experience running agile sprint planning, owning backlogs, managing capacity, and hitting committed sprint goals

  • You are able to balance competing priorities across multiple product lines without losing velocity or quality

  • You are comfortable making decisions with incomplete information in a fast-moving environment

  • You are a clear communicator who can translate technical realities into plain language for non-technical stakeholders

  • You have experience managing or mentoring team members and care about building people up as much as building products

  • You have experience using AI tools to accelerate research, analysis, or product work and are always looking for leverage

Bonus points if:

  • You have familiarity with BigQuery, Looker, or similar data platforms as a product owner

  • You have shipped AI agent products or agentic workflows in a production environment

  • You have experience defining production-ready standards or productization playbooks from scratch

  • You have an agency or consulting background where client needs and product roadmap work had to coexist

 

This might not be the role for you if...

  • You prefer to set strategy and hand off execution, as this role requires you to do the work alongside managing others

  • You're looking for full creative latitude on greenfield products, as this role is about productizing validated innovations and maintaining a portfolio in motion

  • You need detailed requirements handed to you before you can act, as a lot of this role involves defining what "done" looks like for things that don't have a clear spec yet

  • Managing multiple product lines with different stakeholders, cadences, and quality bars at the same time sounds exhausting rather than energizing

  • Data infrastructure isn't something you find genuinely interesting, as you'll be deep in pipelines, connectors, and platform health every week

30/60/90

First 30 Days - Learn

  • Get fully oriented in Seer's data product portfolio: the current state of BigQuery Connectors, Looker dashboards, and AI Agents, and what's in the pipeline

  • Shadow sprint ceremonies and meet with division leads, R&D, and data engineers to understand cross-functional dependencies

  • Identify one backlog item you can personally own and deliver to calibrate on the sprint process

** 60 Days - Build**

  • Own sprint planning end-to-end with a completion rate teams can rely on

  • Produce your first product spec or requirements document from scratch and get it to engineering-ready

  • Take over cross-functional communication so stakeholders have a reliable point of contact for data product timelines

** 90 Days - Lead**

  • Running the sprint cycle independently with clear capacity plans, prioritized backlogs, and predictable delivery

  • Executing at least one R&D-to-production handoff with a maintenance plan in place

  • Contributing to the roadmap conversation with a clear point of view on what to productize, maintain, or sunset

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