I'm Hammad Tariq, a Data & Automation Engineer with 5+ years on AWS, building production data platforms and the automation around them: ETL pipelines, cloud warehouses, backend APIs and AI features that cut cost and scale cleanly.
Hammad Tariq
Data & Automation Engineer
How to scale a data pipeline with RabbitMQ: work queues, a worker pool, durability and backpressure. The pattern that took one pipeline past 2M records a day.
Tools of the trade
Trusted by teams at
Whether it's orchestrating ETL or modelling a cloud data warehouse, the focus is always scalability, observability and clean execution.
I build the AWS data layer a business runs on: ETL/ELT pipelines, cloud warehouses on Redshift and Snowflake, and migrations off creaking on-prem systems. My focus is the unglamorous part: whether the platform still holds up when real data gets messy at 2am. Recent work cut operating costs by up to 99% and turned hours of manual effort into seconds. The AWS-cost lessons from that work are in my guide to cutting an AWS Glue bill.
Orchestrated ETL/ELT with Dagster, dbt and Airflow, from raw ingestion to analytics-ready warehouses.
Data marts and warehouses on Snowflake, Redshift and BigQuery: modelled, governed and query-fast.
Dockerised, CI/CD-driven deployments on AWS with cost optimisation and observability baked in.
Idempotent pipelines, isolated failure domains and graceful degradation, built to survive real data.
Real systems with measured outcomes: the problem, the architecture, and the numbers that moved.
Re-platforming a tier-1 banking EDW onto a cloud-native AWS warehouse
Led the migration of a major retail bank’s on-premises Enterprise Data Warehouse from Sybase to Amazon Redshift on AWS, re-engineering 800+ stored pro…
A 3-tier AWS Glue pipeline for assessment, measurement & analysis data
Built a 3-tier data architecture on AWS for an environmental-solutions provider, using AWS Glue, S3, Redshift, Lambda and PySpark to lift data-process…
Re-architecting on-prem ETL into AWS for 80% better resource utilisation
Led a data-warehouse migration to AWS for a pan-African financial-services group, moving 20–30 ETL scripts and stored procedures onto Glue, Redshift a…
AI Cost Attribution & Real-Time Outage Monitoring from Shared Provider Spend
Restores per-application cost visibility from a single shared AI invoice. A near-zero-touch SDK captures durable usage “receipts”, and invoice-anchore…
AI-Powered Multilingual OCR & Document Intelligence
Converts scanned PDFs and photographed forms into clean, structured Markdown across 10+ languages, including hard scripts like Urdu, Arabic and Amhari…
Hands-off SFTP-to-analytics for daily sales & stock data
An automated ELT pipeline that detects distributor files landing on SFTP, validates and homologates them, and lands analytics-ready data in S3 for rea…
Practical deep dives on data engineering, ETL and analytics: the decisions and the numbers behind real work.
Mask PII in an S3 data lake at the ingest boundary: deterministic hashing that keeps joins working, Glue detection, Lake Formation filters, KMS and erasure.
Why LLM features fail in production when the demo worked: messy real inputs, queues that starve each other, and failures that never raise an alarm.
Fix your AWS bill in-house, buy an automated cost tool, or hire someone? A straight comparison of what each option reaches, and when each is wasted money.
Small, browser-based utilities I built. No signup, and nothing leaves your browser.
A few words from people I've delivered for.
“Skilled, flexible, and fast. Would hire Hammad again.”
“Excellent communication and amazing work with great execution.”
Three ways to engage, from a focused consultation to full end-to-end delivery.
From messy source data to analytics-ready warehouses that cut cost. Let's scope it. I reply within one business day.