Allauddin Shaik · Data Engineer
Reliable data pipelines.Orchestrated and monitored.Built and run in production.
What I've shipped
Products
Invoice Audit Pipeline
The problem
Overcharges and tampered invoices slip through manual review, and money leaks quietly.
The solution
Upload an invoice: every line is checked against your agreed rates, the document inspected for tampering, and you're told exactly what to dispute.
The result
Sample run: 24 line items audited against base rates, $377.80 in overcharges flagged in seconds.
Built on FastAPI · Cloud Run · Firestore · GCS · React
Capabilities
Rate audit
Every line item checked against your agreed rates; overbilling surfaces automatically.
Tamper detection
Catches edits, altered figures, and regenerated pages in the document itself.
Ask any invoice
Plain-language answers about any charge, drawn from the invoice's own data (RAG).
MCP integration
Trigger audits and query invoices from any MCP-enabled assistant: Slack, Teams, and more.
Want this hands-off? I can build the full orchestration layer: the moment an invoice lands in your inbox, it's audited automatically and your team is flagged the instant something looks wrong or fraudulent.
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Track record
Systems that run in production today
Dentsu Global Services
Data Engineer
Remote · 2022–present
Owning the full lifecycle: data pipelines → backend & APIs → application → deployment.
80%
of manual reporting effort eliminated through pipeline automation
70%
cut in annual service costs via a data & dashboard audit
24/7
pipelines running unattended in production on GCP & Azure
- Built a Bayesian media-simulation platform end to end that business users rely on to forecast media performance themselves: designed the data architecture, serverless orchestration, backend services, and the React frontend they use.
- Automated the platform's model builds end to end, and extended the Bayesian simulation logic to support comparative scenario analysis (beyond the original forward-projection model) to meet a new business requirement.
- Built end-to-end orchestration for media-budget optimization: triggered from the UI or automatically when new data lands, running as asynchronous Cloud Run Jobs, writing run status to PostgreSQL and notifying the team on start, completion, and failure.
- Deployed and managed Apache Airflow on GCP for scheduled and on-demand ETL orchestration across the platform's data feeds.
- Designed the analytics data models (OLAP) that made media performance legible across the sales funnel, and built the application layer on top.
- Rolled the platform out across regions from a single codebase (shared business logic, per-country services and databases wired at deploy time through environment config), with a CI/CD pipeline on Azure running unit and integration tests on every change.
- Built a RAG chatbot (LangChain) serving teams real-time, context-aware answers over live product-database records instead of waiting on an analyst.
What I bring
How I work
I don't hand off pieces. I take a problem from raw data to deployed and used: scoped with you, shipped in small increments, and owned through deployment and monitoring.
Data Engineering & Platform
The foundation: data that moves reliably, on infrastructure that runs itself.
- Batch and event-driven pipelines, orchestrated with Airflow, running as serverless jobs on GCP and Azure
- Data architecture: Postgres and cloud storage underneath, with the analytics models (OLAP) that make the data usable
- ML infrastructure: automated model runs, output pipelines, and monitoring around the models; the data scientists own the algorithms
- Deployment ownership: Docker, CI/CD, and monitoring treated as part of the build, not an afterthought
- APIs and the serving layer: FastAPI backends on top of the data, plus production RAG where it adds value; MCP servers and agent workflows are in progress, not yet shipped
Working together
Step 1
We talk
You describe the problem in a 30-minute call. I tell you honestly what will actually solve it, and what it would take.
Step 2
I design and build
A clear plan first, then the system: shipped in small increments you can see, not a black box that goes quiet for weeks.
Step 3
It runs in production
Deployed, documented, monitored. I operate what I build: no handover pain, no babysitting needed.
Contact
Open to new roles, and open for collaboration.
If you're hiring, or you have a system you want built, let's talk.
Based in India · open to relocation, or remote worldwide
