TeamProduct & data · v1.1.0

Data & Analytics Team

Analytics engineering, SQL analysis, dashboard design, experimentation and data quality — organised around metric definitions everyone agrees on.

Analytics engineering, SQL, dashboards, experimentation and data quality.

Includes the metric-definition template that ends the "why do sales and finance disagree?" argument, and an experimentation analyst who will tell you before launch that your traffic cannot support the test.

Agents 5

  1. 01analytics-engineerUse to build and maintain the data model — staging and mart layers, dimensional modelling, transformation pipelines, incremental logic, tests and documentation of the warehouse.
  2. 02dashboard-designerUse to design dashboards and reports — choosing what to show, chart selection, layout, thresholds and alerting — so that a dashboard drives a decision rather than decorating a wall.
  3. 03data-quality-auditorUse to audit data quality — profiling tables, finding duplicates, nulls, broken joins, tracking gaps and definition drift, and diagnosing why two reports disagree.
  4. 04experimentation-analystUse to design and analyse experiments — A/B test design, sample size and power, randomisation and guardrail metrics, results readout, and deciding whether a result is real.
  5. 05sql-analystUse to answer questions with data — writing and debugging SQL, exploratory analysis, funnel and cohort queries, ad hoc investigations, and checking whether a reported number is right.

Skills 2

  1. 01experiment-readoutDesign an experiment or read out its results — hypothesis, sample size, guardrails, validity checks, effect with confidence interval, and a ship/no-ship recommendation. Use for A/B tests, split tests, holdouts and any before/after comparison.
  2. 02metric-definitionDefine a metric unambiguously — formula, grain, denominator, window, filters, edge cases, source of truth and owner — so that everyone computing it gets the same number. Use when a metric is contested, when two reports disagree, or when building a metrics layer.

Commands 2

  1. 01/analyzeAnswer a data question end to end — definitions, profiling, query, validation, segmentation and a decision-ready answer.
  2. 02/dashboardDesign and build a dashboard that drives decisions — reader, metrics, thresholds, layout, charts and alerts.

What this team will not do

It will not report a number without its definition and window, report across a tracking break without flagging it, declare an experiment winner on an underpowered test, present post-hoc segments as findings, or build charts that mislead — no truncated bar axes, no dual axes, no pie charts with many slices. When the data cannot answer the question, it says so and names what would.

€49one-time

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