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Data analyst CVs win or lose on three signals: SQL depth, actual business outcomes moved (not just 'built dashboards'), and the specific stack (Snowflake, BigQuery, dbt). Recruiters skim for numbers and tools.
SQL, dialects matter (Postgres, Snowflake, BigQuery, Redshift)
Python (pandas, numpy, scikit-learn)
R (if the JD mentions it)
dbt, Airflow, Dagster, Prefect
Tableau, Power BI, Looker, Metabase, Superset
Experimentation, A/B testing, statistical significance
Attribution modelling, cohort analysis, funnel analytics
Only include skills you can defend in an interview. Inflated skill lists fail the first phone screen.
Quantified impact: revenue moved, cost saved, time reclaimed, in INR/$/hrs
At least one A/B test or experiment, if relevant
The specific warehouse + BI tool the target company uses
A 'projects' section with a dashboard screenshot link, if you can share
Weak
Built dashboards for the marketing team using Tableau.
Strong
Built the marketing attribution dashboard in Tableau that surfaced a ₹1.2Cr/year leak in Google Ads spend; reallocated budget reduced CAC by 18%.
Weak
Wrote SQL queries to answer business questions.
Strong
Owned all revenue-reporting SQL (Snowflake, dbt) across 200+ tables; reduced daily close time from 2 hours to 10 minutes via incremental models.
Weak
Performed data analysis.
Strong
Ran 3-week A/B test on the onboarding flow; identified a 12% lift (p=0.01) and shipped the winner, adding ~4.5k activated users/month.
A public Kaggle notebook with thoughtful analysis (not just model scoring)
A personal SQL puzzle / leetcode-sql repo
A Substack post analysing a public dataset
A dbt project on GitHub with documentation
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