Senior Data Analyst, Product

What you'll do:

• Serve as a strategic analytics partner to product managers, UX researchers, and engineers to define analytics requirements for new product features, including identifying key user behaviors and success metrics.
• Create scalable product analytics dashboards and self-service tools tailored to product managers, UX partners, engineers, and designers, using SQL, Looker, Mixpanel, Metabase, Python, or R.
• Partner with engineering and analytics engineering to implement and QA product event collection with Segment, ensuring events follow naming conventions, are consistently structured, and are modeled for high-quality downstream analytics; audit event coverage to identify gaps and improve the reliability of behavioral data.
• Conduct deep-dive behavioral analyses (e.g., funnel, cohort, time-to-value, retention, feature adoption) to uncover user needs, friction points, and opportunities to improve the product experience.
• Synthesize quantitative data with qualitative insights to inform product strategy and shape feature roadmaps.
• Build frameworks that measure long-term product engagement, user journeys, and the impact of new features on customer outcomes.
• Collaborate with product teams to design, instrument, and analyze A/B tests and other experimental methodologies; provide interpretation of lift, impact, and risks.
• Support product discovery by developing hypotheses, generating exploratory analyses, and identifying emergent behavioral patterns.
• Validate data accuracy by comparing results across source systems and performing root-cause analysis on anomalies or unexpected metric behavior.
• Enable self-service analytics by educating stakeholders on data sources, metric definitions, and reporting tools; identify opportunities to improve clarity and usability of reporting.
• Partner with Analytics Engineering and Data Engineering teams to define data requirements, improve data quality, and ensure reliable data pipelines and modeling layers.
• Contribute to and uphold team best practices for data modeling, visualization standards, and documentation.
• Participate in peer review processes for data models, dashboards, and analyses, ensuring quality, consistency, and alignment with team standards.
• Share knowledge and emerging best practices with teammates, contributing to documentation and helping strengthen data literacy across the organization.
• Support hiring processes for new analysts by participating in interview loops or technical assessments, as needed.

What you should have:

• 5+ years of experience in data analysis, business intelligence, or a related quantitative field.
• Demonstrated experience in product analytics for a B2B SaaS product, including funnel analysis, retention modeling, cohort analysis, and feature adoption metrics.
• Strong proficiency in SQL, including both ad-hoc querying and data modeling for analytical reporting, especially for event-level datasets and behavioral analytics.
• Strong experience instrumenting and validating product usage event data, preferably using Segment or comparable customer data platforms.
• Experience with at least one data visualization platform (Looker or Metabase preferred) with the ability to design intuitive dashboards and optimize reporting structures.
• Ability to validate data across complex or undocumented systems, ensuring accuracy and consistency across outputs.
• Proven ability to translate product questions into structured analytical approaches that inform feature development and product strategy.
• Experience partnering directly with product managers, UX researchers, and engineers to define metrics, design experiments, and support product discovery.
• Ability to communicate user behavior insights in a compelling narrative that informs product decisions for both technical and non-technical stakeholders.

Nice to haves:

• Experience defining and maintaining a product analytics tracking plan or taxonomy.
• Experience implementing best practices for client-side and server-side event tracking, including schema governance and QA workflows.
• Experience using Mixpanel or a similar tool to visualize user behavior funnels and analyze product dataExperience working with product experiment platforms and A/B testing.
• Familiarity with data engineering or analytics engineering concepts (e.g., dbt, ETL workflows, version control, data model documentation).
• Experience with survey feedback analysis and qualitative analysis.