Product Manager, Data Platform

Beacon Software•Toronto, ON

81d

About The Position

We are looking for a senior to principal product manager to help us build our data platform and own the strategy, roadmap, and execution of Beacon's internal data infrastructure. This is a high-impact, cross-functional role sitting at the intersection of our platform engineering team and our integrations function, which onboards newly acquired companies within 90 days of close. You will be responsible for partnering with our internal platform engineering team to unify the technology stacks of our portfolio companies into a coherent data platform, building agentic workflows that allow internal teams to query and act on data across the portfolio, and developing products that accelerate our BD and M&A function—from data room preparation to tracking acquisition velocity. This is a senior-level role in scope and ownership. This is a rare opportunity to build a data platform from the ground up at a firm that moves fast, acquires companies regularly, and genuinely values the work of platform and product teams. You will have high visibility with senior leadership, direct ownership of a critical internal product, and the opportunity to shape how an entire portfolio of software businesses runs on data. You won't be maintaining someone else's roadmap—you'll be writing it.

Requirements

5–10 years of product management experience, with a meaningful portion spent on data platforms, internal tools, or developer-facing products
Demonstrated experience owning complex, cross-functional product roadmaps with multiple stakeholder groups
Prior experience in a PE-backed, high-growth, or multi-product environment is a strong plus
Background as a software engineer, startup founder, or in a technical IC role is a significant plus

Nice To Haves

Strong technical fluency—you can read code, write lightweight scripts, and hold your own in architecture discussions with engineers
Hands-on familiarity with AI tools, including Claude or similar LLMs, for both personal productivity and product development
Experience building or working with MCP (Model Context Protocol) servers, or demonstrated comfort building agentic AI workflows
Familiarity with data integration patterns, APIs, webhooks, ETL pipelines, and CRM/support platform data models (HubSpot, Zendesk, or similar)
Comfort with SQL and basic data analysis to independently explore datasets and validate product hypotheses

Responsibilities

Define and drive the roadmap for integrating the technology stacks of all Beacon portfolio companies into a unified data platform
Prioritize which data sources, connectors, and integrations to build, establishing a clear framework for sequencing decisions
Coordinate with platform engineering to ensure data infrastructure, internal tooling, and portfolio-facing products converge into a coherent, maintainable architecture
Own the product experience for integration tooling—including templatized onboarding checklists, access tracking, and migration dashboards used by the integrations team
Identify where we need to provide unified dashboards for business intelligence for leadership to help with GM and portfolio reporting analytics
Design and ship agentic workflows that allow internal stakeholders to query, analyze, and act on data across the portfolio in natural language
Evaluate and productize experiments emerging from the GTM engineering team, managing the pipeline from prototype to shipped feature
Champion AI-native product patterns, leveraging tools like Claude and custom MCP (Model Context Protocol) servers to extend platform capabilities
Build and maintain products that ensure clean, up-to-date data is available in the data room for BD and M&A teams at all times
Develop tooling to track and improve BD and M&A velocity—surfacing pipeline metrics, diligence timelines, and acquisition progress across deals
Partner closely with the M&A and Business Development teams to understand their data needs and translate them into platform requirements
Serve as the connective tissue between platform engineering, the integrations team, and business-side stakeholders (BD, M&A, portfolio GMs)
Run structured product discovery, write clear PRDs, and drive sprint-level execution with the engineering team
Define success metrics for the data platform and establish a regular cadence for reviewing progress against them