Data Engineering
Building Cloud ETL Pipelines for Multi-Marketplace Data
A data engineering case study focused on normalizing marketplace data into analytics-ready cloud datasets.
A data engineering case study focused on normalizing marketplace data into analytics-ready cloud datasets.
Overview
Marketplace data often arrives through different APIs, formats, and schema conventions. Analytics work depends on normalized and consistent datasets.
I worked on cloud ETL workflows involving sources such as Walmart, Instacart, SQL Server, and other marketplace integrations.
Problem
The data platform needed to ingest heterogeneous marketplace and database sources, transform them into consistent structures, and load analytics-ready data into BigQuery.
System Architecture
The conceptual flow was:
Marketplace APIs -> ingestion -> AWS Lambda / Python processing -> transformation and normalization -> BigQuery -> analytics
Another known flow moved data from SQL Server through extraction and transformation into BigQuery.
Technical Challenges
The work involved schema normalization, data quality checks, repeatable transformations, serverless processing, and failure handling for integrations that can change or fail independently.
Engineering Decisions
Python provided a practical transformation layer with Pandas and NumPy where tabular processing was appropriate. AWS Lambda fit repeatable cloud ingestion and transformation workloads without requiring a dedicated always-on service.
Outcome
The platform produced more consistent marketplace datasets for downstream analytics while keeping the public description free of unverified row counts, schedules, costs, or private integration details.