Data Infrastructure That Scales.
End-to-end GCP & AWS pipelines, BigQuery warehouses, dbt transformations, and Fivetran/Airbyte ELTs — built for e-commerce brands and SaaS companies that run on data.
Featured Projects(4)
Real work. Real results. Real numbers from real clients.
Amazon Multi-Source Data Warehouse
E-commerce Data Pipeline — Next Degree Products USA
Built a fully automated, end-to-end data warehouse on Google BigQuery consolidating data from 10+ sources — SP-API, Keepa, Sellerboard, Google Ads, and Shopify — giving the operations team a single source of truth for all business decisions.
Data was siloed across 10+ platforms with no unified view. Leadership made decisions based on outdated spreadsheets manually exported each week — costing hours of analyst time and leading to inaccurate reports.
Designed and deployed a BigQuery data warehouse with Fivetran and Airbyte connectors for automated ingestion, dbt for transformation layers, and Cloud Composer (Airflow) for orchestration. Full pipeline refresh every 4 hours.
GCP Data Engineering Pipeline — E-commerce Analytics
Cloud Infrastructure Build — Amazon Seller Central
Architected a full GCP infrastructure for a fast-growing Amazon seller — from raw API ingestion through Pub/Sub, Dataflow transformation, BigQuery storage, and Looker Studio dashboards — replacing a broken Excel-based workflow.
The client relied on manual CSV exports and fragile Excel macros for sales reporting. Data was 3–5 days old, incomplete, and required 2 full-time analysts to maintain. Any breakdown caused total reporting blackout.
Built event-driven pipeline using Pub/Sub → Dataflow → BigQuery. SP-API data ingested in real-time. GCP Cloud Functions triggered on schedule for batch jobs. Dockerised all custom scripts for portability and GitHub Actions CI/CD for zero-downtime deployments.
Keepa & Amazon Keyword Tracker Data Pipeline
Product Research Automation — Market Intelligence
Engineered an automated market intelligence pipeline that ingests Keepa price history, Amazon search volume, and competitor ASIN data into a structured BigQuery warehouse — powering daily product research dashboards for a 7-figure Amazon brand.
Product research team spent 6+ hours daily manually pulling Keepa data, checking competitor prices, and tracking keyword trends. No historical data was stored, making trend analysis impossible.
Built a scheduled Python pipeline using Keepa API and Amazon SP-API to extract ASIN-level data daily. Stored in BigQuery with a normalised schema. dbt models computed competitor price gaps, BSR trends, and keyword rank velocity. Power BI dashboard delivered insights automatically each morning.
AWS EC2 & PostgreSQL Data Infrastructure
Custom Data Stack — SaaS Startup
Deployed a custom data stack on AWS EC2 with PostgreSQL, automated ETL jobs, and a reporting layer for a SaaS startup that needed enterprise-grade data infrastructure at startup budget.
Growing SaaS startup had no data infrastructure — all analytics ran directly on the production database causing slowdowns. They needed a dedicated analytics environment without the cost of a full Snowflake or BigQuery setup.
Set up dedicated AWS EC2 instance with PostgreSQL replica as analytics database. Built Python ETL jobs to sync from production DB every hour. Created schema optimised for reporting (star schema). Configured automated backups, monitoring with CloudWatch, and cost alerts.
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