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

1. 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.
2. 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.
3. Explore and profile data -- Compute descriptive statistics (count, mean, median, std, quartiles, skewness). Check for nulls, duplicates, and outliers before drawing conclusions.
4. 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.
5. 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).
6. 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.