Product Manager, Data Platform

LTK USA

1d • Remote

About The Position

The Data Platform team is transforming how LTK activates data across the company - building the single source of truth that powers analytics, discovery, monetization, and AI. We are modernizing our data stack, standing up an Enterprise Data Model, migrating analytics tooling, and delivering standardized data access services that serve both internal stakeholders and external product surfaces. As a Product Manager on this team, you will own feature areas within the Data Platform, contributing to delivery from spec through launch. You will write clear requirements, collaborate cross-functionally to ensure delivery, and use data and customer feedback to develop your product judgment in a deeply technical domain. This is a craft-focused role for someone who is eager to grow their product skills on foundational platform work, is comfortable learning technical concepts like data pipelines and semantic modeling, and wants to build the muscle of shipping in a complex, cross-functional environment.

Requirements

• 2 - 4 years of product management experience, with exposure to data platforms, data infrastructure, analytics tooling, or backend platform products.
• Ability to write clear specs and collaborate cross-functionally to ensure delivery - you can determine the right level of detail and work with engineering to close gaps.
• Comfort working with data engineering concepts - data pipelines, data warehouses, and semantic modeling. You can write tickets and specs with technical detail and understand system boundaries and trade-offs.
• Developing product judgment and trade-off awareness - you use data and customer feedback to inform decisions and are building the instincts to prioritize effectively.
• Analytical fluency with BI and analytics tools to validate hypotheses and understand how data consumers interact with the platform.
• Cross-functional communication skills - you can collaborate across engineering, analytics, and business teams and are growing your ability to facilitate alignment with multiple stakeholders.
• Comfort navigating ambiguity and competing priorities in a fast-moving environment; you default to action and bring structure where it doesn't yet exist.
• Regularly integrates AI tools into daily workflows to analyze feedback, synthesize insights, and validate outputs with sound prompt design.
• Bachelor's degree in Computer Science, Engineering, Data Science, Business, or related field (or equivalent practical experience).

Nice To Haves

• Experience with modern data stack tooling.
• Familiarity with data governance, data catalogs, or metric definition frameworks.
• Exposure to ML/AI data readiness - preparing or curating data for machine learning pipelines.
• A mindset focused on seizing opportunities and moving with urgency
• Dedication to fierce prioritization and operational excellence
• Adaptability to a dynamic, fast-moving environment
• A growth mindset and openness to feedback

Responsibilities

• Write clear, complete specs - own feature-level specs and one-pagers (problem, goals, requirements, non-goals, dependencies) for initiatives such as Enterprise Data Model domain buildouts, data access services, and analytics tooling enablement.
• Partner closely with Data Engineering to help define, validate, and deliver canonical dimension, fact, and summary tables across Creator, Brand, and Consumer domains.
• Support analytics migrations - assist in the transition from legacy tools to the modern stack, helping ensure business-critical reporting remains uninterrupted and metric definitions stay consistent.
• Support data governance processes - help maintain and enforce processes for how metrics are defined, classified, and consumed, flagging unauthorized data sharing and metric drift to leadership.
• Collaborate cross-functionally - work alongside product teams, I&A, and operational stakeholders to support adoption of platform-owned data rather than bespoke pipelines.
• Use data to inform decisions - track platform KPIs (data freshness, data latency, platform uptime, active users, adoption rate) and use them to validate hypotheses and inform prioritization.
• Help manage intake - triage incoming data requests through defined channels, surface prioritization trade-offs to leadership, and help protect team capacity for high-impact platform work.
• Leverage AI in your workflow - regularly integrate AI tools into daily workflows to analyze feedback, synthesize insights, draft specs, and validate outputs with sound prompt design.

Benefits

• Competitive compensation and benefits package to meet the needs of you and your family
• 401(k) with LTK company matching
• Medical Insurance, Vision Insurance, Dental Insurance
• Paid Maternity Leave and Paid Paternity Leave
• Summer Fridays and Flexible PTO

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level