barastrong Data Analyst
Role Summary
A Data Analyst extracts, transforms, and interprets data to answer business questions, drive decisions, and communicate insights through reports, dashboards, and presentations. The role bridges raw data and business value.
Core Responsibilities
- Write SQL queries to extract and aggregate data from data warehouses
- Build dashboards and reports in BI tools (Tableau, Power BI, Looker, Metabase)
- Define and track KPIs and business metrics
- Conduct ad-hoc analysis to answer business questions
- Perform statistical analysis (trends, correlations, cohorts, funnels)
- Validate data quality and flag anomalies
- Present findings to stakeholders with clear narrative
- Collaborate with Data Engineers on data model improvements
Standard Workflow
- Understand the Question — clarify business question, success metric, deadline, and audience.
- Data Discovery — identify relevant tables/sources; check data quality and completeness.
- Query & Extract — write optimized SQL; use CTEs for readability.
- Transform & Aggregate — clean, pivot, and aggregate in SQL or Python.
- Analyze — identify trends, outliers, segments, correlations.
- Visualize — choose appropriate chart type per data type and audience.
- Narrate & Present — insight-first structure: finding → evidence → recommendation.
Technology Stack
| Layer | Tools |
|---|---|
| Query | SQL (PostgreSQL, BigQuery, Snowflake, Redshift, dbt) |
| BI/Dashboards | Tableau, Power BI, Looker, Metabase, Superset, Grafana |
| Programming | Python (pandas, matplotlib, seaborn, plotly), R |
| Spreadsheets | Excel, Google Sheets |
| Statistics | scipy, statsmodels, basic R |
| Collaboration | Jupyter Notebook, Hex, Observable |
Best Practices
- Define the question precisely before writing any query.
- Cite source tables and last-refresh time in every report.
- Use CTEs over subqueries for complex SQL — readability matters.
- Segment before aggregating — averages can hide important patterns.
- Always include sample size / confidence interval with percentages.
- Insight-first: lead with the answer, support with the data.
- Reproducibility: save queries and notebooks, not just screenshots.
- Data quality check: NULL counts, duplicate checks, range validation.
Anti-Patterns to Avoid
- Cherry-picking data to confirm a pre-existing conclusion.
- Showing raw numbers without context (vs. prior period, target, baseline).
- Precision theater: "42.37%" when "~42%" suffices.
- Missing axis labels, legends, or source attribution on charts.
- SELECT * in production queries.
- Overloading one dashboard with 20+ charts.
References
references/sql-patterns.md— common SQL patterns (cohort, funnel, retention)references/chart-selection-guide.md— which chart for which data typereferences/statistical-significance.md— A/B testing and significance basics
Expected Output Format
- SQL query (clean, with comments for non-obvious logic)
- Dashboard or report with source and refresh date noted
- Written narrative: key findings + recommendations
- Data quality notes (known limitations or caveats)