Build the projects you’ll open in your next job interview.
Twenty-seven projects on Indian data, from a first pipeline to an AI agent, each with a brief you see first.
Taught live by industry experts from Mastercard and Paytm.
Data Engineering · week 22Runs nightly
Market (Mandi) Price PipelineThe crop-price table traders open each morning, filled overnight and safe to rerun.
Airflow · Docker · Snowflake · Slack
A hiring manager looks for three things in a project.
Every project here is built to have all three, and the faculty review it before it goes on your GitHub.
A real-world problem
Built on the messy data a company would hand you: crop prices with gaps, toll records, festival sales, complaints in Hindi and English.
A result you can measure
One number shows it works: the same row count after a rerun, a better answer score, or hours saved in a week.
Code anyone can run
A public repository with one command to start it, so a stranger can run it on their own laptop.
Find the projects you want on your resume.
Pick a program, a level or a tool. Starter projects run on your laptop. Core projects add what a company runs every day: schedules, tests and alerts. Advanced projects run in the cloud or as a live service.
No project matches all three. Try another level or tool.
How every project gets built, from the brief to the panel.
Every figure here comes from the project's committed run log, which you can read line by line.
- 01Read the briefWhat to build, on which data, and what done looks like.
- 02Build it liveOn screen with the faculty, chat open, your question answered on the call.
- 03Prove it runsA log of the run that shows it is done: a count, a score or a week of runs.
- 04Get it reviewedAgainst the brief, with the one thing to fix named.
- 05Present itFor the end-to-end project, live, to people who have hired for the role.
- 06Put it on your resumeOne project entry, with a link to the code.
See a verified run in full: the Market (Mandi) Price Pipeline →
The project list and the tools, for every program and course.
- Every brief, by program and level
- What a finished build looks like, for each
- The data each one uses, and the stack in order
- Which program and week builds each one
Download it now. A program advisor calls within one working day to walk through it and match a project to where you start.
Your project list and tools are on the way.
A program advisor calls within one working day to walk through it.
How a project reads on your resume.
Name it, list the tools, prove it with one number, and link the code. Here is one project, written the way hiring managers read it.
Market (Mandi) Price Pipeline1Python, Apache Airflow, Docker, Snowflake2
github.com/your-name/mandi-price-pipeline4
- Built a nightly Apache Airflow pipeline that loads daily market (mandi) crop prices, in the Agmarknet format, into Snowflake; containerised with Docker.
- Made every load idempotent: backfills and reruns add zero duplicate rows.3
- Added validation and retries that stop empty, missing and out-of-range prices before load, with a Slack alert on any failed night.
What a hiring manager checks
- 1A title that says what it doesThe name tells them the problem before they read a line.
- 2The tools they search forWritten the way job posts write them, so a recruiter’s search finds you.
- 3One number that proves itA count, a score or a time, measured on your own run.
- 4A link they can openYour repository, with a README and a diagram of how the parts fit.
Shipped my first production-style pipeline: market crop prices into Snowflake every night, checked before they load, and safe to rerun. The code and the run log are on GitHub.
inFeatured on her profile
Every PG Program ends in one big project you present to a panel.
It is the last thing you build, and every project before it feeds into it.
Your own data platform
Weeks 33–34 · presented to a panelBatch and real-time data on your cloud, with an AI layer on top and its monthly running cost worked out to the rupee.
Proves you can build, run, cost and explain a data platform.
See the program →PG Program · AI EngineerYour own AI product
Weeks 33–34 · presented to a panelAn AI product in the track you pick in week 33: deployed, monitored, and its cost per 1,000 requests worked out.
Proves you can build, ship, run and cost an AI product.
See the program →PG Program · AI GeneralistThe Adoption File
Weeks 19–22 · presented to a panelOne process from your own job, measured before and after, and presented as a business case a finance head can sign.
Proves you can find where AI moves a number, and prove it.
See the program →Every project is set and reviewed by industry experts who build these systems at work.
The faculty teach each project live, review every build and answer in the cohort chat between sessions, by name.
Meet them before you apply: try a free live build, ninety minutes, live and free.
Start your application.
Three short steps. Step one takes a minute; finish the rest now or later.
Details saved.
Two short steps finish the application, now or later. A program advisor calls within one working day either way.
Your application is in.
A program advisor calls you within one working day with the timetable, the faculty list and the cohort calendar.
Data engineering and AI projects: your questions, answered.
What makes a data engineering project good enough for a resume?
The three things a hiring manager looks for, at the top of this page: a real-world problem, a result you can measure, and code anyone can run. The faculty review every project here against them.
Are these end-to-end data engineering projects?
The three end-to-end projects take raw data to a running, costed system, presented live to a panel. The projects along the way each build one layer of that system, and every one of them feeds into the end-to-end build.
Do these data engineering projects come with source code on GitHub?
Every data engineering project you build lives in your own GitHub repository, with a README and its run log. The source code is yours to show an interviewer. The briefs are in the free project list.
Can I build these data and AI projects without joining a program?
You can start from the briefs in the free project list today. The live sessions with the faculty, the weekly reviews, the Saturday doubt clinic and the panel come with the program.
Which data engineering projects are best for beginners?
Start with a Starter project: the Kirana Ledger, the first project of both Data Engineering Fundamentals and the PG Program. The Market (Mandi) Price Pipeline is the next step: one nightly pipeline, run on a laptop for ₹0.
Is AI replacing data engineers?
AI writes code faster; companies still hire the engineer who makes a pipeline right and keeps it running. Every AI product runs on data somebody moved, cleaned, tested and scheduled, which is why the Data Pipeline for an AI Assistant is a project here.
Which cloud should my data engineering projects use?
The one your target employers use. The program's projects land on AWS, Azure or Google Cloud, picked when the cloud weeks begin, and week 13 maps the whole build onto the other two.
Which machine learning projects should go on a resume?
Two or three that prove different things: a model argued in rupees (the Credit Call), an AI assistant with a measured score (the Policy Navigator), and a deployed service with its cost stated (the Model in Production). A fresher's resume gains most from the first two.
What are good AI agent projects for a resume?
An agent that acts on a system and is tested against attack: the Policy Navigator gets an agent, with LangGraph, MCP tools and a red-team checklist. Without code, the Customer Support Agent in n8n shows the same judgement: an approval step before anything irreversible, and a log of every decision.
How do I write a data engineering project on my resume?
Name it, list the tools, prove it with one number and link the code, as the resume on this page shows. Portfolio reviews in every program check each line against what a hiring manager opens first.
Do I need GitHub for my data and AI projects?
Yes, for the coded ones: a hiring manager opens the repository before the call. Every coded project here is built in a personal repository from week one, with a README a stranger can follow.
How do these projects help with placement?
They are what placement support works from. Portfolio reviews check them against what an interviewer opens first, and mock interviews are built on them. On the PG Programs you also present the end-to-end project to a live panel, and get introductions into the 39+ companies our learners work at.
A portfolio is projects somebody can check. Build yours beside the industry experts who set them.
Pipelines on your own cloud, models deployed and measured, or AI running inside your own job: every project reviewed against a brief you see first, and an end-to-end project presented to a panel.
Not ready to apply yet? Request the project list and tools or save a seat at the free live build.
Data and AI openings on WhatsApp, and the hiring market twice a month.
The roles the companies our learners work at are hiring for, and the dates of the next free live build. Read three issues of the Brief and you’ll know whether we’re your kind of school.
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The first alert lands the next time our network is hiring, and the next Brief lands in your email.