Product Analyst Job Description Template

KPI Frameworks · 22%
The role is responsible for defining metrics that teams can align on and trust for product decisions. Evaluated in Round 1.

• Weak: Summarizes metrics without defining clear ownership, definitions, or success criteria.
• Meets the bar: Builds a metric framework with documented definitions and recommends a small set of measurable KPIs.
• Strong: Leads cross-functional alignment on metric definitions and success criteria, anticipating edge cases (e.g., attribution/windowing) and ensuring consistent usage.

SQL for Product Data · 22%
Independent analysis requires reliable SQL to extract, join, and aggregate complex event datasets. Evaluated in Round 1.

• Weak: Writes queries that miss edge cases or require frequent rework due to incorrect joins/filters.
• Meets the bar: Produces correct queries for funnels/cohorts and validates results with reasonable sanity checks.
• Strong: Optimizes for correctness and clarity, handles data pitfalls (deduping, time windows, nulls), and produces reproducible outputs for stakeholders.

Measurement Planning · 18%
Instrumenting and validating event tracking is essential to ensure downstream analysis is accurate. Evaluated in Round 2.

• Weak: Gives generic tracking feedback without specifying event properties or validation steps.
• Meets the bar: Defines measurement plans for key flows including event names, properties, and QA checks.
• Strong: Audits existing tracking, identifies gaps, proposes precise fixes, and establishes validation that prevents regressions before analysis uses the data.

Experiment and Funnel Analysis · 18%
The role needs to interpret funnel and experiment results to drive product changes. Evaluated in Round 2.

• Weak: Describes experiments at a high level without addressing key assumptions or interpreting results carefully.
• Meets the bar: Runs funnel/cohort analyses and explains results with clear assumptions and segmentation.
• Strong: Designs robust analyses (segmentation, guardrails, and statistical considerations), translates findings into decision-ready recommendations, and communicates confidence/limitations.

Analytics Dashboards · 10%
Dashboards and reporting are required for trustworthy, scalable visibility across stakeholders. Evaluated in Round 3.

• Weak: Creates reports without consistent definitions or validation, leading to stakeholder mistrust.
• Meets the bar: Builds dashboards that use agreed metrics and include helpful context (filters, definitions, refresh expectations).
• Strong: Creates self-serve reporting that reduces back-and-forth, includes data quality signals, and ensures metric consistency across teams and time windows.

Insight Communication · 10%
The role must influence product decisions through clear storytelling and actionable recommendations. Evaluated in Round 3.

• Weak: Presents analysis without a clear recommendation or tailored context for the audience.
• Meets the bar: Communicates findings with supporting evidence and a specific next step or recommendation.
• Strong: Leads stakeholder-ready narratives: frames the question, shows the key evidence, quantifies impact and uncertainty, and aligns tradeoffs to product goals.