This article describes a Data Analyst role focused on turning complex data into clear insights that drive product decisions, user growth, and operational efficiency. It outlines the role overview, core responsibilities across teams, required technical and analytical skills, and preferred experience that supports scalable data practices in product and growth environments.
Role overview and cross-functional impact
The Data Analyst will transform complex datasets into actionable insights and work cross-functionally with product, engineering, marketing, and leadership. The role supports a strong data-driven culture by informing product decisions, supporting user growth initiatives, and improving operational efficiency through clear, data-backed recommendations.
Core responsibilities
- Collect, clean, and validate data from databases, APIs, and third-party platforms to ensure reliable inputs for analysis.
- Write optimized SQL queries (PostgreSQL) to extract actionable insights and support downstream reporting.
- Design and maintain dashboards tracking KPIs such as user acquisition, activation, retention, churn, revenue, and engagement.
- Perform cohort analysis, funnel analysis, and trend analysis to surface behavioral patterns and opportunities.
- Define and track key product and growth metrics (DAU, MAU, CAC, LTV, conversion rates) to measure performance.
- Analyze A/B test results and provide statistically sound recommendations based on hypothesis testing.
- Ensure proper event tracking across web and mobile applications and identify anomalies or data inconsistencies.
- Work with engineering teams to maintain normalized, structured, and reliable datasets and implement data validation checks.
- Automate recurring reports and build scalable reporting pipelines to support ongoing decision-making.
- Present insights clearly to stakeholders with data-backed recommendations that inform product and growth strategies.
Required skills and how they apply
- SQL (PostgreSQL preferred): Strong proficiency to extract and manipulate data for analysis and reporting.
- Data visualization: Experience with tools such as Metabase, Apache Superset, Power BI, or Tableau to design and maintain dashboards for KPIs.
- Statistics and hypothesis testing: Solid understanding to analyze experiments, A/B tests, and provide sound recommendations.
- Spreadsheet modeling: Excel or Google Sheets for modeling and quick exploratory analysis.
- Basic Python: Familiarity with Pandas and NumPy or similar tools for advanced analysis when needed.
- Product analytics understanding: Knowledge of retention, churn, and conversion funnels to translate business questions into measurable metrics.
- Analytical thinking: Strong problem-solving ability to identify anomalies, define validation checks, and derive insights from data.
Preferred experience and technical context
- Experience working in a startup or high-growth environment to support rapid product and growth needs.
- Knowledge of ETL processes and basic data pipeline concepts to help build scalable reporting systems.
- Familiarity with REST APIs and backend data flow concepts to support data collection and integration efforts.
- Experience supporting product and growth teams to align analysis with business priorities and stakeholder needs.
In summary, this Data Analyst role combines data collection, rigorous analysis, and clear communication to support product decisions, growth metrics, and operational efficiency. Candidates should bring strong SQL and visualization skills, statistical knowledge, and the ability to work cross-functionally, with preferred experience in startup environments and basic ETL and API familiarity.
