Business Intelligence
Custom Power BI and Tableau dashboards, goal trackers, and interactive conversion charts.
Clean data pipelines, KPI dashboard reports, and SQL databases optimized for querying. We turn chaotic tables into interactive dashboards and reports that founders can act on.
WITH MonthlyRevenue AS (
SELECT
DATE_TRUNC('month', created_at) AS revenue_month,
SUM(amount) AS mrr,
COUNT(DISTINCT tenant_id) AS active_tenants
FROM billing_records
WHERE status = 'paid'
GROUP BY 1
)
SELECT
revenue_month,
mrr,
active_tenants,
mrr / active_tenants AS arpu
FROM MonthlyRevenue
ORDER BY 1 DESC;
import pandas as pd
import sqlalchemy
def clean_and_load(conn_string, csv_path):
engine = sqlalchemy.create_engine(conn_string)
df = pd.read_csv(csv_path)
# Drop null rows & format currencies
cleaned_df = df.dropna(subset=["user_id"])
cleaned_df["amount"] = cleaned_df["amount"].fillna(0)
cleaned_df.to_sql("billing_records", engine, if_exists="append")
{
"$schema": "https://vega.github.io/schema/vega-lite/v5.json",
"data": { "url": "/api/analytics/mrr" },
"mark": "line",
"encoding": {
"x": { "field": "revenue_month", "type": "temporal" },
"y": { "field": "mrr", "type": "quantitative" }
}
}
Engineered Features
Custom Power BI and Tableau dashboards, goal trackers, and interactive conversion charts.
Automated ingestion pipelines configuring Python script transformations and dbt steps.
Index setups, query tuning, and read replica configurations to eliminate database lag.
Calculations of customer MRR, lifetime values (LTV), and multi-month user retention charts.
Connect SQL databases directly to Excel and Google Sheets tables with automated syncs.
Secure cloud data warehousing setups on Snowflake and Google BigQuery environments.
Technology Stack
Build scopes
One main database dashboard connected to your core records, with basic data parsing and 5 custom key performance metrics.
Custom Python/dbt ETL pipelines, cohort databases, daily background schedules, and 3 analytics dashboard reports.
Configured BigQuery or Snowflake workspace, dbt transformation rules, security role divisions, and high-frequency reporting.
Deployment process
Frequently Asked
We architect high-speed columnar data models using ClickHouse, DuckDB, or PostgreSQL Materialized Views paired with Redis query caching. This allows dashboards to calculate aggregations across millions of rows with sub-80ms rendering speed.
Yes. We build automated ETL / ELT data ingestion pipelines connecting Stripe, Shopify, Google Ads, Meta Ads, HubSpot, Mixpanel, and SQL databases into a unified data warehouse with automated nightly or real-time sync.
We implement strict Row-Level Security (RLS) and role-based permissions. Executive leadership can see company-wide gross profit metrics, while department managers or external clients only see metrics relevant to their specific workspace.
Yes. Every dashboard includes 1-click clean export for filtered CSV and Excel tables, plus scheduled cron digests that deliver formatted PDF performance summaries to your leadership team's inboxes or Slack channels weekly.
All sensitive customer and revenue data is encrypted with AES-256 at rest and TLS 1.3 in transit. We implement PII anonymization and masking filters to ensure full compliance with GDPR, HIPAA, and SOC2 guidelines.
Yes. We build cohort retention matrices, customer churn likelihood classifiers, customer lifetime value (LTV) projections, and statistical anomaly detection algorithms that alert you when revenue or traffic deviates from forecast norms.
Explore other services
Let's map out your databases and key business KPIs in a 30-minute discovery call.