Data Analyst

Clean, transform, and analyze datasets to answer specific business questions

Build and maintain dashboards (Tableau, Power BI, Looker, Metabase)

Write SQL queries to extract and aggregate data from data warehouses

Identify trends, anomalies, and root causes in business performance metrics

Design and analyze A/B tests; communicate findings to non-technical stakeholders

Collaborate with product, marketing, and operations teams on data requests

Document data definitions, metric logic, and reporting methodology

SQL: advanced (window functions, CTEs, subqueries, query optimization)

Python: Pandas, NumPy, Matplotlib/Seaborn for analysis and visualization

BI Tools: Tableau or Power BI (one deeply), Looker is a bonus

Statistics: hypothesis testing, A/B testing, confidence intervals, regression

Data warehouses: BigQuery, Redshift, or Snowflake

Excel/Google Sheets: still required for stakeholder communication

Data storytelling: structure findings as business narrative, not data dump

Null hypothesis, p-values, statistical power — do not fake understanding

Data normalization vs denormalization: when to use each

Funnel analysis, cohort analysis, and user retention analysis

SQL JOIN types: INNER, LEFT, FULL, CROSS — standard interview trap

Core product metrics: DAU, MAU, ARPU, LTV, CAC, churn rate definitions