Product Analyst

5 days ago

Islamabad, Islamabad, Pakistan Digital Finance International Full time 900,000 - 1,200,000 per year

We're looking for a hands-on analyst who combines
data skills
(SQL, tracking, dashboards) with
product sense
: you connect data to user behavior and business goals, propose what to measure and why, and make concise recommendations on what to do next.

Responsibilities:

  • Establish
    product analytics from zero: design the event schema and tracking plan, run instrumentation QA, maintain a metric dictionary, and
    decide where dashboards will live
    (e.g., Metabase/Tableau/Power BI).
  • Query & report:
    write reliable SQL, automate recurring reports, and build/maintain dashboards.
  • Report on and validate core metrics:
    funnels, DAU/WAU/MAU, activation, retention/churn & cohorts, unit economics (CAC/LTV/Payback), ensure definitions and calculations are correct.
  • Define success upfront:
    help choose the North Star and input metrics for features/experiments, set targets, and interpret results.
  • Communicate clearly:
    provide weekly/monthly updates — in writing and verbally — explaining in plain language what happened, why, and where to focus next.
  • Design and run experiments:
    A/B tests, segmentation, cohort reads; document hypotheses, guardrails, and success criteria.
  • Accelerate product decisions:
    maintain accurate metrics and crisp summaries to enable fast trade-offs (briefs, QA, alerts).
  • Collaboration:
    primarily partner with the PO on data and metrics; collaborate with Engineering/Design as needed to support discovery and delivery.

Requirements:

·      Exceptional SQL and production-oriented data modeling (star/snowflake, SCD2, snapshot tables, clear grain and keys).

  • Hands-on product tracking (GA4/Amplitude/Mixpanel or similar) with disciplined event taxonomy.
  • BI proficiency (Tableau/Power BI), including scheduled reports/alerts.
  • Strong understanding of product & business metrics (funnels, cohorts/retention, unit economics); able to weigh alternative explanations and recommend next steps.
  • Clear writing and synthesis (one-pagers, memos, concise decks).
  • Experimentation mastery: design of experiments, A/B testing, segmentation, cohort analysis.
  • English: B2 or higher.

Nice to have

  • Python for ad-hoc analysis/notebooks.
  • Experience in fintech, banking, or financial marketplaces.
  • Standing up analytics in a greenfield environment.
  • AI literacy: understanding modern LLMs/agentic AI and using them to speed up analysis and reporting.

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