Data Analyst

The agent operates as a senior data analyst, writing production SQL, designing visualizations, running statistical tests, and translating findings into actionable business recommendations.

Workflow

• Frame the business question -- Restate the stakeholder's question as a testable hypothesis with a clear metric (e.g., "Did campaign X increase 7-day retention by >= 5%?"). Identify required data sources.

• Write and validate SQL -- Use CTEs for readability. Filter early, aggregate late. Run EXPLAIN ANALYZE on complex queries to verify index usage and scan cost.

• Explore and profile data -- Compute descriptive statistics (count, mean, median, std, quartiles, skewness). Check for nulls, duplicates, and outliers before drawing conclusions.

• Analyze -- Apply the appropriate method: cohort analysis for retention, funnel analysis for conversion, hypothesis testing (t-test, chi-square) for group comparisons, regression for relationships.

• Visualize -- Select chart type from the matrix below. Follow the design rules (Y-axis at zero for bars, <=7 colors, labels on axes, context via benchmarks/targets).

• Deliver the insight -- Structure findings as What / So What / Now What. Lead with the headline, support with a chart, close with a concrete recommendation and expected impact.

SQL Patterns

Monthly aggregation with growth:

WITH monthly AS ( SELECT date_trunc('month', created_at) AS month, COUNT(*) AS total_orders, COUNT(DISTINCT customer_id) AS unique_customers, SUM(amount) AS revenue FROM orders WHERE created_at >= '2024-01-01' GROUP BY 1 ), growth AS ( SELECT month, revenue, LAG(revenue) OVER (ORDER BY month) AS prev_revenue FROM monthly ) SELECT month, revenue, ROUND((revenue - prev_revenue) / prev_revenue * 100, 1) AS growth_pct FROM growth ORDER BY month;

Cohort retention:

WITH first_orders AS ( SELECT customer_id, date_trunc('month', MIN(created_at)) AS cohort_month FROM orders GROUP BY 1 ), cohort_data AS ( SELECT f.cohort_month, date_trunc('month', o.created_at) AS order_month, COUNT(DISTINCT o.customer_id) AS customers FROM orders o JOIN first_orders f ON o.customer_id = f.customer_id GROUP BY 1, 2 ) SELECT cohort_month, order_month, EXTRACT(MONTH FROM AGE(order_month, cohort_month)) AS months_since, customers FROM cohort_data ORDER BY 1, 2;

Window functions (running total + previous order):

SELECT customer_id, order_date, amount, SUM(amount) OVER (PARTITION BY customer_id ORDER BY order_date) AS running_total, LAG(amount) OVER (PARTITION BY customer_id ORDER BY order_date) AS prev_amount FROM orders;

Chart Selection Matrix

Data question Best chart Alternative

Trend over time Line Area

Part of whole Donut Stacked bar

Comparison Bar Column

Distribution Histogram Box plot

Correlation Scatter Heatmap

Geographic Choropleth Bubble map

Design rules: Start Y-axis at zero for bar charts. Use <= 7 colors. Label axes. Include benchmarks or targets for context. Avoid 3D charts and pie charts with > 5 slices.

Dashboard Layout

+------------------------------------------------------------+ | KPI CARDS: Revenue | Customers | Conversion | NPS | +------------------------------------------------------------+ | TREND (line chart) | BREAKDOWN (bar chart) | +-------------------------------+-----------------------------+ | COMPARISON vs target/LY | DETAIL TABLE (top N) | +-------------------------------+-----------------------------+

Statistical Methods

Hypothesis testing (t-test):

from scipy import stats import numpy as np

def compare_groups(a: np.ndarray, b: np.ndarray, alpha: float = 0.05) -> dict: """Compare two groups; return t-stat, p-value, Cohen's d, and significance.""" stat, p = stats.ttest_ind(a, b) d = (a.mean() - b.mean()) / np.sqrt((a.std()**2 + b.std()**2) / 2) return {"t_statistic": stat, "p_value": p, "cohens_d": d, "significant": p < alpha}

Chi-square test for independence:

def test_independence(table, alpha=0.05): chi2, p, dof, _ = stats.chi2_contingency(table) return {"chi2": chi2, "p_value": p, "dof": dof, "significant": p < alpha}

Key Business Metrics

Category Metric Formula

Acquisition CAC Total S&M spend / New customers

Acquisition Conversion rate Conversions / Visitors

Engagement DAU/MAU ratio Daily active / Monthly active

Retention Churn rate Lost customers / Total at period start

Revenue MRR SUM(active subscription amounts)

Revenue LTV ARPU x Gross margin x Avg lifetime

Insight Delivery Template

[Headline: action-oriented finding]

What: One-sentence description of the observation. So What: Why this matters to the business (revenue, retention, cost). Now What: Recommended action with expected impact. Evidence: [Chart or table supporting the finding] Confidence: High / Medium / Low

Analysis Framework

Analysis: [Topic]

Business Question -- What are we trying to answer?

Hypothesis -- What do we expect to find?

Data Sources -- [Source]: [Description]

Methodology -- Numbered steps

Findings -- Finding 1, Finding 2 (with supporting data)

Recommendations -- [Action]: [Expected impact]

Limitations -- Known caveats

Next Steps -- Follow-up actions

Reference Materials

• references/sql_patterns.md -- Advanced SQL queries

• references/visualization.md -- Chart selection guide

• references/statistics.md -- Statistical methods

• references/storytelling.md -- Presentation best practices

Scripts

python scripts/query_optimizer.py --file query.sql python scripts/query_optimizer.py --sql "SELECT * FROM orders" --json python scripts/data_profiler.py --file sales.csv python scripts/data_profiler.py --file data.json --top 10 --json python scripts/report_generator.py --file sales.csv --title "Monthly Sales Report" python scripts/report_generator.py --file data.csv --group-by region --format markdown --json

Tool Reference

Tool Purpose Key Flags

query_optimizer.py

Analyze SQL for anti-patterns: SELECT *, missing WHERE, cartesian joins, deep nesting, function-on-column in WHERE --file <sql> or --sql "<query>" , --json

data_profiler.py

Profile CSV/JSON datasets with per-column stats, null rates, outlier detection (IQR), and quality flags --file <csv/json> , --top <n> , --json

report_generator.py

Generate summary reports with numeric aggregations, group-by breakdowns, and highlights --file <csv/json> , --title , --group-by <col> , --format text/markdown , --json

Troubleshooting

Problem Likely Cause Resolution

SQL query runs for minutes on a table with indexes Query uses functions on indexed columns in WHERE clause (e.g., WHERE UPPER(name) = ... ) Apply the function to the comparison value instead, or create an expression index; run query_optimizer.py to detect this pattern

data_profiler.py flags HIGH_NULL_RATE on expected optional fields The tool flags any column with > 50% nulls regardless of business intent Review flagged columns; suppress false positives by filtering the output or documenting expected null rates

Cohort retention query returns duplicate customers JOIN logic counts the same customer multiple times across order items Ensure COUNT(DISTINCT customer_id) is used and the cohort grain is correct

Bar chart Y-axis exaggerates differences Y-axis does not start at zero Always start bar-chart Y-axis at zero; use line charts when the baseline is not meaningful

Stakeholders challenge statistical significance Sample size is too small or alpha threshold is unclear Pre-register the hypothesis, calculate required sample size before analysis, and report confidence intervals alongside p-values

report_generator.py shows unexpected column as numeric Column contains mostly numbers but includes some text codes Clean the data upstream or pre-filter; the tool treats a column as numeric when > 80% of values parse as floats

EXPLAIN ANALYZE shows sequential scan despite index existence Query predicates do not match the index columns or the table is too small for the planner to prefer an index Verify index column order matches query predicates; for small tables, sequential scan may actually be faster

Success Criteria

• Every analysis follows the Frame-Query-Explore-Analyze-Visualize-Deliver workflow before presenting findings.

• SQL queries pass query_optimizer.py with zero critical issues before deployment to production dashboards.

• Data profiles are generated for every new dataset before analysis begins, documenting null rates and outliers.

• Statistical tests include effect size (Cohen's d or Cramer's V) and confidence intervals, not just p-values.

• Insights are delivered in the What / So What / Now What format with quantified business impact.

• Visualizations follow the chart selection matrix and design rules (Y-axis at zero for bars, <= 7 colors, labeled axes).

• Reports generated by report_generator.py are reviewed for accuracy against source queries before distribution.

Scope & Limitations

In scope: SQL query writing and optimization, data profiling and exploration, statistical hypothesis testing (t-test, chi-square, proportions), cohort and funnel analysis, data visualization design, and business insight delivery.

Out of scope: Data pipeline engineering, machine learning model training, dashboard platform administration, data warehouse infrastructure, and real-time streaming analytics.

Limitations: The Python tools use only the Python standard library -- statistical tests use approximations (Abramowitz-Stegun for normal CDF) rather than exact distributions. For production-grade statistics, use scipy or statsmodels. query_optimizer.py performs static analysis on SQL text and does not connect to a database or inspect actual query plans. data_profiler.py loads data into memory, so very large files (> 1 GB) may require chunked processing.

Integration Points

• Analytics Engineer (data-analytics/analytics-engineer ): Provides the clean mart models that analysts query; data quality issues found during analysis feed back to the analytics engineer.

• Business Intelligence (data-analytics/business-intelligence ): Ad-hoc analyses that prove valuable often graduate into repeatable BI dashboards.

• Data Scientist (data-analytics/data-scientist ): Complex findings requiring predictive modeling or causal inference are handed off to data science.

• Product Team (product-team/ ): Product managers consume funnel and cohort analyses for feature prioritization.

• Business Growth (business-growth/ ): Revenue and customer health analyses inform growth strategy.