Senior Data Analyst, Product Analytics

Analytical Tools Implementation: Partner with developers/platform architects to deploy relevant product analytics solutions (like Pendo) and ensure the right metrics, journeys, and events are captured in-product.

Data-Driven Decision Making: Use product analytics/data to recognize patterns and trends to understand user behavior and identify improvement opportunities.

Measurement Frameworks: Proactively establish measurement frameworks/success metrics for new product launches/features and drive actionability through data-driven insights.

AI-Powered Analytics: Apply AI-native analytics approaches, using LLMs to analyze unstructured data, leveraging RAG-based retrieval concepts to synthesize information across knowledge bases (hands-on implementation not required), and using GenAI tools to automate reporting and surface insights, with clear human-in-the-loop review, QA, and validation practices to ensure accuracy, reliability, trust and appropriate use.

Stakeholder Management: Independently develop and manage relationships with peers/product managers/senior leadership for your portfolio ensuring collaborative partnership for OKR setting and driving analytical insights.

Center of Excellence: Partner with product teams to define and maintain support processes and instill a data-driven culture within the organization.

BI/Reporting: Partner with BI teams and other analysts to build dashboards and reports, including baselines and trends for page performance, feature usage, and customer segments.

Thought Leadership: Demonstrate thought leadership by proactively leveraging analyses to understand products, derive insights, run experiments, and be the contact point for any analytics need for your portfolio.

5+ years of hands-on product analytics experience that includes experience in analytics measurement, working with stakeholders, and influencing specific decisions, OKRs, or metrics.

5+ years of experience independently driving data-driven decision-making within product teams going beyond report generation to deliver insights through data storytelling. Experience with analyses such as customer journey mapping, audience profiling/segmentation, cohort analysis, tag management, and A/B testing.

4+ years of experience using SQL building and documenting queries for consistent team use.

4+ years of experience with dashboarding techniques to make complex data more understandable and insightful using tools such as Tableau and PowerBI.

4+ years of experience using statistical packages within Python/R to analyze structured and unstructured datasets and deliver insights on product usage and performance.

Strong understanding of statistical concepts including hypothesis testing, confidence intervals, correlation analysis, regression analysis, and statistical significance to ensure rigorous data interpretation.

Demonstrated ability to leverage AI tools for analytics, including designing prompts to extract insights from LLMs, using generative AI for data interpretation and insight synthesis, and utilizing RAG systems for information retrieval and knowledge management.

AI Insight Quality & Governance: Establish lightweight QA and review practices for AI-assisted analytics (validation sampling, traceability to sources, prompt/version documentation) to ensure insights are accurate, explainable, and decision-ready.

Strong people skills and stakeholder intuition, able to plug into existing product/engineering processes, influence without authority, and stay relentlessly curious to understand the "why" behind the data.

Exposure to product analytics tools such as Pendo, Amplitude, Adobe Analytics, and Google Analytics is an asset.

SaaS experience is an asset.