About Introhive
Introhive is an AI-powered Relationship Intelligence platform that helps firms break down data silos and unlock actionable relationship insights that drive collaboration and growth.
Since launching in 2012, we’ve grown significantly, but our mission remains the same: to transform how companies manage, nurture, and leverage relationships to unlock value, accelerate growth, and delight customers.
Introhive is one of the fastest-growing B2B relationship intelligence platforms. We’re recognized as a category leader in sales intelligence and data quality management software by G2, named a Top 10 Fastest Growing Technology Company in Deloitte’s Fast 50 Awards three years running, and awarded the 2020 MarTech Breakthrough Award for Best CRM Innovation.
Trusted by industry-leading brands including KPMG, Freshfields, CBRE, and Deloitte, Introhive supports more than 250,000 users across 90+ countries.
Job Summary
As a Senior Product Data Analyst on Introhive’s Product Operations team, you will be a force multiplier for the Product Development organization by helping to build, scale, and steward the product insights capability. You will turn product usage metrics, customer feedback, and other structured and unstructured data into actionable insights that drive roadmap, improve product outcomes, and enable strategic bets. Partnering closely with Product Managers, Engineering, UX Research, and Go-to-Market (GTM) teams, you’ll standardize our internal product data taxonomy, improve analytics instrumentation and tooling, operationalize our feedback loops, and enable truly data-informed decision-making across the product lifecycle.
Location: Remote within Canada.
Job Responsibilities
Analyze user behaviour and outcomes to drive decisions
- Run funnel, cohort, retention, time-to-value, and feature adoption analyses; pinpoint drop-offs and root causes.
- Segment analytics by persona, account tier, industry, and/or region to illuminate who succeeds (or struggles) and why.
- Combine quantitative signals with VoC (Voice of the Customer) data to produce decision-ready recommendations with expected impact.
Own product analytics platform & metrics framework
- Administer and evolve Pendo: event taxonomy, instrumentation to close gaps, governance, and data quality.
- Define standards for key product metrics at the product/feature level.
- Build and maintain dashboards for product health and usage, ensuring quality and self-serve capabilities.
Drive VoC & GTM feedback loop data strategy
- Automate ingestion of product insights, unifying inputs from GTM, support, surveys, and call transcripts.
- Implement simple data pipelines to manage data in a robust, repeatable way, ensuring clear lineage and high data quality.
- Implement taxonomy and process for feedback to be efficiently categorized, summarized, and prioritized in a data-driven manner.
- Implement AI workflows (e.g., topic/intent extraction, clustering, summarization, anomaly detection) where feasible, with human-in-the-loop review and appropriate compliance guardrails.
Investment & portfolio insights
- Contribute to the product commercialization process by helping to define and measure product outcome metrics for product investments to test hypotheses early-on, then to assess ROI on Beta/GA launches.
- Provide portfolio-level recommendations—what to scale, fix, or stop—based on impact and confidence.
- Conduct analysis to identify opportunities and validate direction for both near-term product improvements and larger strategic bets.
Enable stakeholders with product analytics
- Deliver self-serve dashboards, and/or training to raise analytics fluency; and help GTM team members use analytics as part of renewal and expansion motions.
Experimentation & product-led growth
- Collaborate with product managers to design hypothesis-driven experiments to increase activation, stickiness, and expansion; define success and guardrail metrics, do analysis, and make recommendations.
- Collaborate with product managers to identify and test growth levers with measurable outcomes.
Qualifications
- Analytical Depth: Expertise in product analytics (funnel, cohort, retention, segmentation), experimentation, and causal thinking.
- Data Fluency: Proficient with SQL, a BI tool (e.g., Tableau/Looker/Power BI), cloud data warehouses (e.g., Snowflake), and at least one product analytics platform (e.g., Amplitude/Mixpanel/Pendo) or equivalent.
- AI-Augmented Analysis: Skilled at using LLMs and AI tools to accelerate analysis and operations (e.g., generating SQL/code safely, summarizing usage/VoC, clustering themes, triaging feedback, and narrating dashboards).
- Synthesizing Insight from Data: Comfortable combining quantitative signals with qualitative research to tell a cohesive story.
- Data Storytelling: Exceptional at building decision-ready narratives—clear visuals, plain language, and crisp recommendations.
- Data Instrumentation & Taxonomy: Experience defining event schemas, tagging strategies, and ensuring data quality at scale.
- Collaboration: Strong communication and stakeholder management; able to align PM, Engineering, UX, CS, and GTM around metrics and insights.
- Experimentation Mindset: Familiar with hypothesis design, guardrail metrics, and statistical concepts (e.g., confidence intervals, p-values, power).
- Organizational Skills: Pragmatic, organized, and with demonstrated experience managing multiple parallel initiatives and prioritizing effectively.
Education and Experience
- 5+ years of experience in data analysis, product operations, or adjacent roles.
- Bachelor's degree in Computer Science, Engineering, Statistics, Business, or a related field.
Why Introhive?
At Introhive, we believe people do their best work when they feel trusted, empowered, and supported. That’s why we value outcomes over hours, curiosity over perfection, and collaboration over ego. You'll have room to grow, tools to succeed, and a team that’s got your back.