Data Engineering · APIs · AI · Automation
Building data pipelines that connect Amazon commerce sources to validation, analytics, automation and AI-assisted investigation.
AT A GLANCE
CONTEXT & PROBLEM
An international e-commerce operation used Amazon Ads, Seller Central and Vendor Central across approximately 800 accounts and all marketplaces. Making that information usable downstream required a shared path from API ingestion to normalized business data in BigQuery.
Reports and API data arrived with different schemas and reporting cadences. The platform needed to expose missing periods, stale last-available dates, incomplete or inconsistent fields and source-to-warehouse discrepancies without hiding where each issue originated.
MY ROLE
Within the broader platform, I contributed to ingestion and transformation work and owned the Python comparison pipeline used to investigate differences between Amazon reports and BigQuery data.
Contributed to Python and SQL pipelines for Amazon data ingestion, standardization and transformation.
Analyzed data coverage, freshness, missing dates, consistency and API-response anomalies across platform layers.
Built the Python pipeline that compared Amazon reports with BigQuery data and generated discrepancy reports.
Added OpenAI-assisted analysis to the discrepancy workflow while preserving the underlying engineering checks.
SIMPLIFIED DATA ARCHITECTURE
Conceptual and anonymized view; implementation details are intentionally omitted.
ENGINEERING APPROACH
The L1/L2/L3 architecture separated source ingestion from transformation and business consumption. Recurring pipelines included confirmed daily loads for part of the Amazon environment, while validation remained part of the data path.
Used Python and Amazon APIs to bring source data into a BigQuery-based platform.
Worked with L1/L2/L3 layers so source-aligned, transformed and business data remained distinguishable.
Applied SQL transformations and normalization before data reached analytics and automation consumers.
Used telemetry and targeted checks to inspect expected periods, missing dates, last-available dates, completeness and consistency.
ENGINEERING CHALLENGES
Challenge → why it mattered → responseFOCUSED IMPLEMENTATION
Data quality & discrepancy detection
SOLUTION
CORE TECHNOLOGY
EVIDENCE-BASED IMPACT
Recorded outcomes from the project scope.
Approximately 800 accounts were within the platform scope across all marketplaces.
66 Ecombrain modules were mapped to understand platform coverage; these were modules, not datasets.
Recurring ingestion and transformation included confirmed daily loads for part of the Amazon environment.
Established a repeatable comparison between Amazon source reports and BigQuery data.
Made coverage, freshness, missing fields and consistency issues traceable before downstream consumption.