Data Engineering

Retail Sales Data Pipeline

Daily raw sales files into clean marts for business reporting.

Pipeline mode

Batch

Output grain

Daily sales

Focus

Reliability

Context

Problem

Raw daily sales exports were scattered across files and hard to reconcile for weekly reporting.

Solution

Designed a batch ingestion flow, normalized transaction tables, and prepared analytics-ready marts for sales, product, and store performance.

Tools

Python / PostgreSQL / SQL / dbt basics / Power BI

Journey

  1. 01Profile raw CSV exports and map missing values, duplicates, and late-arriving records.
  2. 02Load raw files into staging tables with repeatable Python jobs.
  3. 03Transform staging data into fact sales and product/store dimensions.
  4. 04Validate totals, row counts, and key business metrics before exposing marts.
  5. 05Connect the final mart into a dashboard-ready reporting layer.

Output

  • Sales mart for daily revenue and order analysis.
  • Product and store dimensions for drill-down reporting.
  • Data quality checklist for every pipeline run.

Try the result

Open a dashboard-style preview showing revenue trend, top products, and exception checks.