Build a Governed Shopper Segmentation Pipeline with Python
Group: Use Case
Product Category: Cloud & Data Engineering
Sub Category: Data Platform Engineer
Solution Available
Source code, datasets, and other related files for the solution will be provided.
Prerequisites
About this Product
NovaCart Retail Inc. is an online marketplace for home and lifestyle goods. Its Marketing & Personalization team wants to run targeted campaigns such as loyalty offers, win-back emails, and VIP perks, but lacks a repeatable, governed way to transform raw shopper data into a trustworthy segment table. Today, an analyst manually assembles the data in a spreadsheet, making the process slow, error-prone, and difficult to audit.
In this lab, you build a small governed data pipeline based on standard data-lake zoning: a raw zone for landed source files, a curated zone for validated and deduplicated records, and a consumption zone for marketing queries. You ingest two flat files, customers.csv and orders.csv, into the raw zone, validate and deduplicate them into the curated zone with a detailed reject log, compute per-customer metrics such as total spend and days since last order, and assign each customer to one of five segments: VIP, Regular, New, At-Risk, or Inactive. Segment thresholds are read from a YAML configuration file rather than hardcoded.
A single Python orchestrator runs ingestion, curation, and segmentation in strict order, halting on the first failing stage. A lightweight role-permission check enforces which service role can read or write each zone, while per-stage run logging provides an audit trail.
You will practice designing storage zones, validating file presence and business rules, deduplicating records by primary key, computing aggregations and joins with pandas, externalizing configuration to YAML, implementing role-based access control, and orchestrating a multi-stage pipeline with fail-fast behavior and run logging.
By the end, you will have a working, tested pipeline with sample data, run logs, and a consumption-ready shopper_segments table committed as evidence of a successful run. You will also create a short skill-mapping write-up that can be used as interview talking points and portfolio material.
Interview Q&A Included
1 practice set bundled with this lab
-
Governed Shopper Segmentation Pipeline Assessment
Category: Cloud & Data Engineering Subcategory: Data Platform Engineer 27 questionsThis assessment checks what you learned while building the governed shopper segmentation pipeline for NovaCart Retail. It is split into five groups: the business scenario, business and technical rules, pipeline …
Resources
The Shopper Segmentation Pipeline starter project is available as a downloadable attachment. It provides the project structure, setup, documentation, and supporting files required to begin the implementation. Use this project as your starting point to build the solution based on the requirements defined in the Business Requirements Document.
Asset Contents:
Project structure and source-code scaffolding
Initial configuration and setup files
Starter interfaces, classes, functions, or components
Tests and validation setup, where applicable
Documentation and project instructions
Sample data or supporting resources, where applicable
Required dependencies and environment configuration
Implementation tasks and TODOs, where applicable
Topics
Skills
Languages
English
Level
Beginner
Business Domain
Retail and Customer Personalization
Project Mentors
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