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data engineering · beginner → senior

Master the DataEngineering Systems.

Target audience
  • Engineers preparing for senior Data Engineering interviews.
  • Working engineers looking to improve data system design on the job.
  • Beginners who want to learn data engineering from scratch.
Start buildingExplore knowledge
01
decision loop
03
flagship studios
05
foundation modules
500+
practice scenarios
1000+
interview questions
Flagship Studios

Build systems.
Defend decisions.

studio 01

Data Pipeline

Draw the DAG, configure every node, route the failures.

Try the pipeline studio — free, no account ▶ How it works — a 5-minute guided tour
studio 02

Data Modelling

Set the grain, the keys and the history. Then defend it.

Try the modelling studio — free, no account ▶ How it works — a 5-minute guided tour
studio 03

Spark Playground

Your PySpark on the left. The DAG, plan, stages and tasks Spark actually ran for it, on the right.

Explore the Spark studio ▶ How it works — a 5-minute guided tour
Data engineering jobs · 100 companies · fresh every 6 hours

Pick a role.
Get the plan.

Live roles from the careers pages of 100 companies, each read for the skills it asks and turned into a practice plan you can start today. Then you apply on their site, not ours.

MastercardStaff8+ yrstoday

Lead Data Engineer (Data Platforms)

Pune, IndiaAWSAzureSparkGovernance & securityMastercardSeniortoday

Senior AI Data Engineer

Pune, IndiaAirflow / orchestrationData modellingData qualityPythonNebiusSenior8+ yrstoday

Senior Data Engineer

Tel Aviv, IsraelAirflow / orchestrationCost & performanceData qualityIdempotency & backfills
See all live rolesHow a plan is built
Knowledgebase Library

Know what
you're building.

01 / knowledge base

SQL

Reason about grain, joins, windows, and query cost.

02 / knowledge base

Python

Write transformations that are readable, tested, and correct.

03 / knowledge base

Spark

Understand shuffles, skew, memory, partitions, and job behavior.

04 / knowledge base

Data Modelling

Choose grain, keys, facts, dimensions, history, and ownership.

05 / knowledge base

Data Pipeline

Plan ingestion, orchestration, idempotency, and recovery.

the loop

Build.
Review.
Defend.

Every scenario runs the same three beats — the loop a senior engineer runs in a design review, and the one an interviewer walks you through.

01

Build

on the canvas

Drag real services onto the canvas, wire the DAG, and configure every node — write mode, triggers, delivery guarantees.

02

Review

code decides

A deterministic engine scores structure, requirements and reliability, lists what's working, and ranks what to fix first. Then the AI narrates it — grounded in your graph.

03

Defend

in your words

Then the follow-ups, one probe at a time, on the model you actually drew — the same questions that decide a senior interview.

Reviews · engineers on the job, and two who just got one

What it did
for them.

Working data engineers, a data automation engineer, and two freshers who landed their first data role.

The Spark playground shows the DAG for the code I just wrote. Five years in, and the shuffle finally clicked.

RahulData Engineer · 5+ yearsDunnhumby · India

Modelling reviews that argue back. Grain, keys, fan-out.

UditData Engineer · 5+ yearsDunnhumby · India

Pipelines that fail like production does. Idempotency and backfills stopped being interview words.

NikhilAI Data Automation Engineer · 6+ yearsThermo Fisher · Germany

Zero to Data Engineer was my whole roadmap. Read a little, build a little, and it added up to a job.

ShashwatSDE-1, Data · fresherInfosys · India

Got the job. SQL drills did it.

PrashantSDE-1, Data · fresherInfosys · India
start

Your first system is one click away.

Pick a scenario, draw it, and find out what a reviewer would say.

Open a studio Take the 5-minute tourBrowse the knowledgebase