Senior Analyst, Product Analytics

Double Good•Chicago, IL

16h•Hybrid

About The Position

Serve as the senior data analyst for Double Good's Product function, bringing rigorous analysis and data evidence to every product decision: measure product and feature changes, size and prioritize opportunities, read experiments, and pinpoint where organizers, sellers, and buyers stall. Double Good is a fundraising app paired with our own gourmet popcorn kitchens. Youth teams and schools fundraise in a few clicks, we cook and ship directly to supporters, and half of every dollar goes back to the youth organization. This role expands the Analytics & Insights capabilities of Double Good as the dedicated senior analyst and thought partner to the Product function. It owns the analytical agenda for the product: how each product and feature change performs for organizers, sellers, and buyers, how success is measured, and what ships next. It turns product questions into plans for analysis and discovery and sets the standard for how Product uses data. It is an individual-contributor role partnering daily with Product Managers, Design, and Engineering. The work splits roughly 20% describe, 65% diagnose, and 15% predict: certified product metrics are kept current and consumed on Mondays; most of the week goes to reading experiments and diagnosing where users stall; the remainder sizes opportunities and forecasts the impact of what ships next. Success is a roadmap ranked on data evidence and experiments read with rigor.

Requirements

4+ years in product analytics for a consumer app or e-commerce product, with a proven record of measuring the impact of product changes and influencing roadmaps.
Expert SQL on a modern cloud data warehouse (Snowflake preferred); working knowledge of dbt.
Hands-on experimentation: test design, sample sizing, success and guardrail metrics, and honest readouts.
Fluency in product-analytics and eventing data (Amplitude, Mixpanel, or Segment) and in Looker or an equivalent BI tool.
Experience defining, instrumenting, and quality-checking product metrics with Product Managers and Engineers, edge cases included.
Storytelling with data: clear written and verbal communication with product and executive stakeholders.
Drives ambiguous projects forward independently.
Bachelor's degree in statistics, economics, mathematics, computer science, or a related quantitative field, or equivalent experience.

Nice To Haves

Experience with a multi-role product (marketplace, creator, or fundraising platform) serving several user types.
Experience in direct-to-consumer e-commerce or CPG with repeat customer engagement dynamics.
Proficiency in Python or R, with a statistical foundation in hypothesis testing and funnel and cohort analysis.
Causal inference beyond A/B testing (difference-in-differences, synthetic control, uplift modeling).
Mobile app analytics, including app-store, push, and in-app messaging data.
Time-series forecasting of product metrics.
Prior hands-on dbt or LookML development; Git-based workflows.
Pairing quantitative analysis with qualitative research (user interviews, support feedback) to explain the why.
Use of AI and LLM tools to accelerate analysis and summarize findings.

Responsibilities

Define how product success is measured: adoption, engagement, completion, and conversion metrics, set with Product and certified in Looker with Business Intelligence and Analytics Engineering.
Size and prioritize opportunities: quantify reach, impact, and confidence so Product Managers rank the roadmap on evidence.
Own experiment measurement: partner with Product Managers on test design, define success and guardrail metrics, size samples, and deliver the readout with a clear ship, iterate, or stop recommendation; uphold experimentation standards across Product.
Pinpoint user hurdles: funnel, cohort, path, and segmentation analysis within each product flow, showing where organizers, sellers, and buyers stall, drop, or return.
Define the measurement spec: specify the events and properties each launch must capture, partner with Engineering to implement, and validate the data before the first readout.
Measure and forecast product impact: quantify how shipped product and feature changes moved adoption and completion, and estimate the effect of roadmap items on fundraiser success and organizer growth, with Data Science where models add precision.
Run the product analytics backlog and learning agenda: prioritize by impact on Product metrics and keep exploratory dashboards current so Product Managers self-serve routine questions.
Deliver recommendations, not just reports: translate product questions into data problems and answers into clear, actionable plans for Product leadership.