Product Analyst

At Supertails, the Product Analyst will work closely with Product Managers and Engineering

teams to enable data-driven product decisions. This role requires strong hands-on capability

in SQL, Python, BigQuery, and data visualization, along with a deep understanding of user

behavior and product performance.

The analyst will be responsible for building reliable analytical datasets, conducting deep

product analyses, and delivering insights that directly influence product roadmap, feature

prioritization, and experimentation.

Key Responsibilities

Partner with Product Managers to define product metrics, success criteria, and

analytical frameworks.

Analyze user behavior across core product flows such as discovery, cart, checkout, and

repeat purchase.

Perform funnel, cohort, retention, and feature adoption analyses.

Write advanced SQL to build reusable analytical datasets in BigQuery.

Develop and maintain analytics-focused transformation logic.

Use Python for exploratory analysis, experimentation analysis, and automation.

Design and analyze A/B tests in collaboration with Product and Growth teams.

Build dashboards and reports using data visualization tools.

Ensure data correctness, performance, and scalability of analytical queries.

Collaborate with Engineering to ensure accurate event tracking and instrumentation.

Must Have

Minimum 2 years of experience in a Product Analyst or Product Analytics role.

Strong proficiency in SQL including complex joins, CTEs, and window functions.

Hands-on experience with BigQuery or similar cloud data warehouses.

Working knowledge of Python for data analysis and experimentation.

Experience with data visualization tools such as Looker, Tableau, Power BI or GA4.

(Amplitude, Mixpanel will also be considered)

Strong understanding of product metrics, funnels, cohorts, and experimentation.

Ability to independently own analytical problems end-to-end.

Good-to-Have

Experience designing event schemas and analytics instrumentation.

Familiarity with dbt, Airflow, or modern analytics pipelines.

Experience working in B2C, D2C, or marketplace businesses.

Exposure to personalization, lifecycle, or recommendation analytics.

Understanding of statistical concepts and hypothesis testing.