New to code? Start here.
A data engineering course for beginners: SQL, Python and your first data pipeline in 8 weeks, live.
For anyone starting from zero: AI assistants first, then SQL, Python and Git, and the Kirana Ledger on your GitHub, with placement support and a certificate an employer can verify.
Taught live by industry experts from Mastercard and Paytm.
★ 4.7 from our pilot batch · 4,200+ learners trained by SkilD · learners at 39+ companies
Free · 90 minutes, live with our faculty · next date announced soon
Thirty people on the same weeks, with one chat for questions and a mentor who knows your name.
The Kirana Ledger, and three smaller builds on the way to it.
Resume, portfolio review, mock interview, job alerts and introductions.
Named after your project, so an employer sees the work.
Data engineer salary in India: up to ₹80 LPA.
Every AI product runs on a pipeline somebody built, and companies pay well for the people who build them.
- ₹8.5 LPAAverage, first 2 years
- ₹18 LPAAverage, 2–5 years
- ₹32 LPAAverage, 5–8 years
- ₹46 LPAAverage, 8+ years
- ₹80 LPATop 10%, 8+ years
recrew.ai India Data Engineer salary guide, 2026 (averages by experience; ₹80 LPA is the top 10% at 8+ years) · PlugScale GCC benchmark, 2026 · 6figr, India.
Who Data Engineering Fundamentals is for.
People starting from zero who want a first data role, and a working project to show for it in eight weeks.
A fresher or student→ A first data role
Start from a blank screen. You finish with SQL, Python and a project an interviewer can open.
An analyst on Excel→ SQL and Python
You already work with data. Here it moves out of spreadsheets and into a database, and the table rebuilds itself on a schedule.
Back after a break→ A fresh start
You restart with the skills data teams are hiring for now, alongside people at every stage.
Anyone switching in→ A tested first step
Eight weeks to find out if data engineering is your road, with a project in hand either way.
- No coding experience needed
- Your weekend mornings for eight weeks, live
- A laptop with 8 GB memory; every tool is free
- English you can follow in a live class
PG Program in Data Engineering: 34 weeks to the Data Engineer role.
The full program adds the cloud, Spark, Airflow, Kafka and dbt, an end-to-end data platform presented live to a hiring panel, and placement support through to offer. Finish this course first and you start it with SQL, Python and a working pipeline already in hand.
Fifteen tools, free to start, the same ones data teams use every day.
Installed with the helpline in week one, before the first live session.
- Any laptop from the last five years, with 8 GB memory
- PostgreSQL, Python and VS Code, installed with the helpline
- A free GitHub account, where your project lives
- Everything runs on a laptop: no cloud account or card needed
Stuck on setup? WhatsApp the helpline and someone walks you through it, step by step. Every tool stays on your laptop after the course.
Write it. Run it. Break it. Fix it.
The full Data Engineering Fundamentals syllabus: eight weeks in five parts, and one new skill at the end of each.
AI Launchpadweeks 1–2 · 12 hrsYou leave able toUse the main AI assistants safely, and check what they tell you.
- W1LLM foundations and the major AI assistantsLarge language model fundamentals: tokens, context windows and hallucination · ChatGPT, Claude, Gemini and Copilot compared · prompt engineering: role, context, few-shot examples and output format · output verification, and AI data security: confidential data, PII and acceptable-use policy6 hrs
- W2AI-assisted research, spreadsheet analysis and a first workflowResearch with citations in NotebookLM and Perplexity · AI in spreadsheets: formula generation and data analysis · image and video generation (demonstration) · an AI workflow, built liveBuild: a working AI workflow with a verification log6 hrs
SQLweeks 3–4 · 12 hrsYou leave able toWrite the SQL that data jobs and interviews ask for.
- W3SQL queryingSELECT, filtering and joins · aggregation with GROUP BY · subqueries and common table expressions (CTEs)6 hrs
- W4Analytical SQLWindow functions · indexing and query execution plans · a training table built on the SkilD lab PostgreSQLBuild: a timed SQL set on a business schema6 hrs
Pythonweeks 5–6 · 12 hrsYou leave able toWrite clean, tested Python that loads, cleans and reshapes data.
- W5Python fundamentalsData types, functions, file I/O and exception handling · unit testing with pytest · REST APIs and JSON6 hrs
- W6Data manipulation with pandas and NumPypandas · NumPy · data cleaning and transformationBuild: a tested data-cleaning script6 hrs
Linux and Gitweek 7 · 6 hrsYou leave able toWork the way engineering teams do, with an AI assistant you keep in check.
- W7Linux, Git and AI-assisted codingLinux shell: bash, file permissions and cron scheduling · Git branching, pull requests and the GitHub workflow · AI-assisted coding in a version-controlled repository, with review controlsBuild: a repository with a clean history6 hrs
Project weekendweek 8 · 6 hrsYou leave able toBuild the Kirana Ledger end to end, and show it working.
- W8Project weekend · the Kirana LedgerCapstone pipeline build, mentor-led · project review and assessment · catch-up session for missed classesBuild: the Kirana Ledger, from database to clean table, on a schedule6 hrs
The Kirana Ledger · Data Engineering Fundamentals
A pipeline that runs on a schedule and reruns without duplicating a row, and resume lines to explain in any interview.
The detailed Data Engineering Fundamentals syllabus and the timetable, in one PDF.
- Every topic, week by week
- The timetable, with dates
- All the tools, and the setup guide
- The Kirana Ledger brief
Download it now. The helpline is on WhatsApp if a question comes up.
Your syllabus is ready.
Questions? The helpline answers on WhatsApp.
Taught live by industry experts who build data pipelines for a living.
They answer in the cohort chat between sessions, and review the work you hand in.
An 8-week plan, mapped with a program advisor.
Get your 8-week plan: a program advisor calls within one working day and maps your route to a first data role. Or WhatsApp the helpline now.
Your plan is on its way.
A program advisor calls within one working day to map the route to a first data role.
Three things that make eight weeks work.
Hands-on, from week one
Every week ends in something built on your own laptop, reviewed by the faculty.
The tools data teams use
SQL, PostgreSQL, Python, pandas, Git and ten more, free to start.
Built around a full-time job
Weekend mornings, and every class recorded and up within 24 hours.
What a weekend looks like, and the help around it.
Everyone moves through the same week on the same deadline. The project weekend doubles as a catch-up clinic for anyone who missed a class.
- 01The brief, firstWhat you’ll build this week, and what done looks like. Before the session, not after.
- 02Build it with the facultyOn screen with the faculty, chat open, your question answered on the call.
- 03Finish itStarted in the session, finished by the deadline, pushed to your GitHub.
- 04Reviewed, every weekYour work checked by the expert who set it, so you know where you stand.
- 05Bring what brokeAsk in the cohort chat, or bring it to the next session.
- 06It carries forwardEvery week hands its work to the next. The SQL from week 3 answers the shop owner in week 8.
A data engineering project for your resume: the Kirana Ledger, a pipeline you build that runs on a schedule.
It lives on your GitHub, and it is finished when it runs on its own and a rerun changes nothing. An interviewer can open it and run it.
The Kirana Ledger: a shop’s books, loaded on a schedule by code you wrote.
A three-shop kirana business sends its sales as CSV and JSON files, with the usual mess: repeated rows, blank prices, dates written three ways. Your pipeline cleans them, loads them into PostgreSQL on a schedule and answers the owner’s five questions in SQL: what moved fastest, what ran short, which days are slow, which customers come back, and what the month made.
- The data
- Eighteen months of the shops’ sales files, supplied with the brief: synthetic, shaped on a small retailer’s day, messy on purpose.
- Done when
- It runs on a schedule, a rerun leaves the row count unchanged, and the five answers come straight out of SQL.
- What it proves
- That you can take raw files to clean, queryable tables, and keep them that way.
- What it does
- A nightly batch pipeline from raw sales files to analytics-ready tables.
- Tech stack
- SQLPostgreSQLPythonpandascronGit
- Key challenge
- Data quality checks that stop a bad file before it loads.
Three builds on the way
Each one becomes a piece of the Kirana Ledger.
The Timed SQL Set
Business questions answered in SQL on a shop’s schema, against the clock, with window functions where they help.
Done when: every answer matches the check totals.
Proves the ability to turn a question into a query.
The Data-Cleaning Script
A raw export cleaned in pandas, with pytest checks on every rule, written back as a clean table.
Done when: the tests pass and the row counts reconcile from file to table.
Proves the ability to handle the files data teams receive.
A Repository With a Clean History
The project in Git, built on branches and pull requests, with an AI coding assistant kept in check and a README a stranger can follow.
Done when: a stranger can clone it and follow the README.
Proves the habits of an engineering team.
The faculty who teach it review every project.
Placement support: five steps from the course to your first data role.
The Kirana Ledger is the raw material. These five steps put your project in front of hiring teams.
- 1Week 8Your resume and LinkedInRewritten around the Kirana Ledger and reviewed line by line.
- 2Week 8Your portfolio reviewYour GitHub checked against what an interviewer opens first.
- 3After week 8A mock interviewAn SQL and Python round with an industry expert, recorded and reviewed.
- 4From week 8Job alertsEntry-level data openings on WhatsApp, as they open.
- 5From week 8IntroductionsTo hiring teams in the SkilD network, as roles open.
Check your resume against these roles first → · Going for the Data Engineer role? See the PG program →
Our learners work at 39+ companies.
Where people who learned with SkilD work now.
amazon
Swiggy
Twitter
ByteDance
PayPal
Flipkart
ATLASSIAN
Training a team? 12+ organizations bring us in to train their own people, on their own work. Plan a program for your team →
Every AI system in the country runs on a pipeline somebody built.
Models get the headlines. Pipelines get the pager.
Every AI product shipping now runs on data somebody moved, cleaned, tested and scheduled, and the companies racing to ship AI are hiring the people who build that layer. This course is where you start on it.
From a first query to a first data role.
I had never written a query. By week four I was answering my manager’s questions before he finished asking.
The helpline got my PostgreSQL install working before the first session. After that, every week felt within reach.
I took it to find out if data was for me. The Kirana Ledger answered that, and I joined the PG program.
One data engineering certificate an employer can verify by its ID.
Awarded when your project is complete and every build passes its review, with a verification ID anyone can check.
Awarded by SkilD on completion, and checked at skild.co.in/verify. It names the Kirana Ledger, so an employer sees the work itself, with the ID to prove it.
One fee for the data engineering course, everything included.
About ₹208 a live hour.
- All eight weeks, taught live
- The Kirana Ledger and three builds on the way
- Placement support
- Every session recorded
- The weekday doubt channel and the Saturday doubt clinic
- The certificate
Training a whole team? Plan it with us →
Why eight weekends, live?
Plenty of ways to learn SQL and Python. Here is what each one usually gets you.
| The route | What it usually gets you | What 8 weeks here gets you |
|---|---|---|
| YouTube and free courses | Everything, in the wrong order, and nobody to ask when it breaks. | A fixed order, and the faculty answering in the session. |
| A recorded course | Videos you mean to finish. | A live session every weekend, a deadline that keeps you moving, and placement support after. |
| A three-day bootcamp | A certificate and a blur. | Eight weekends, each one built on the last, so it sticks. |
From enrolment to day one, in five steps.
- 1EnrolTwo short steps on this page
- 2Pay onceYour seat is held for this cohort
- 3Join the cohort groupOn WhatsApp, with your batch
- 4Setup callWith the helpline, before day one
- 5First live sessionOnline, with your faculty
10 AM to 1 PM IST. Week 1 opens with SQL on real files.
Seat confirmed. Welcome to the November cohort.10:02
- ✓PostgreSQL
- ✓Python
- ✓VS Code
- ✓Git
The joining link for Saturday is pinned above.Fri 6:00 PM
- One price, everything includedevery live session and its replay, every project review, and placement support.
- Live means livea room of thirty, every question answered in the session.
- Dates that holdplan your months around us.
- A mentor who knows your namefirst reply within one working day.
- Miss a weekwatch the recording, then bring your questions to the Saturday doubt clinic.
- Everything you build is yourscode, projects, portfolio. For good.
Enrol in Data Engineering Fundamentals.
Two short steps, then payment. About three minutes.
Details saved.
One more short step, then payment.
You are enrolled.
Your confirmation and the cohort WhatsApp link arrive within minutes. The helpline sets you up before day one.
Data engineering course for beginners: your questions, answered.
Do I need to know how to code for this data engineering course for beginners?
The course starts from zero: AI assistants in weeks 1 and 2, your first SQL query in week 3 and Python from a blank screen in week 5. The helpline sets up the laptop before the first session.
What will I be able to do at the end of Data Engineering Fundamentals?
Query a database in SQL, clean messy files in tested Python, work in Git like an engineering team, and run a small pipeline on a schedule that reruns without duplicating a row. The Kirana Ledger on your GitHub shows all of it working.
Is this data engineering course enough to get a job?
It prepares you for entry roles like Data Analyst, Junior Data Engineer, ETL Developer and SQL Developer, with placement support to reach them. For the Data Engineer role itself, the PG Program in Data Engineering goes further: the cloud, Spark, Airflow and Kafka, and an end-to-end data platform.
Why does a data engineering course start with AI tools?
Because data teams now work with AI assistants every day. Two weeks on using them well, and checking what they say, makes the SQL and Python that follow faster to learn.
Is Python or SQL easier to learn first?
SQL, for most beginners: it reads close to English and answers a question from the first hour. That is why this course teaches SQL in weeks 3 and 4, then Python, once you already think in tables.
Who can enrol in Data Engineering Fundamentals?
Anyone who can follow a live class in English: students, freshers, working professionals and people coming back after a break. Every stream and every degree is welcome.
Can I join this SQL and Python course from a non-tech background, like B.Com or BA?
Yes. The course starts from zero, so a commerce, arts or science graduate begins in the same place as an engineer, and every tool is installed with the helpline.
What laptop do I need for the data engineering course?
Any laptop from the last five years with 8 GB memory, on Windows, Mac or Linux. Everything runs on the laptop itself.
Can I do this data engineering course while working full time?
Yes. Classes run on weekend mornings, every session has a replay within 24 hours, and the project weekend doubles as a catch-up clinic.
What are the data engineering course fees?
₹9,999 including GST, once. It covers all eight weeks taught live, the Kirana Ledger and three builds, placement support and the certificate.
What if I miss a session of the data engineering course?
Every class is recorded and up within 24 hours. Watch it, then bring your questions to the Saturday doubt clinic.
Do I get a data engineering certificate?
Yes, with a verification ID an employer can check. It is awarded when your project runs and every build passes its review.
Is there placement support after the data engineering course?
Yes, in five steps: your resume and LinkedIn, a portfolio review, a mock interview, job alerts and introductions to hiring teams in the SkilD network, as roles open.
Does Data Engineering Fundamentals lead into the PG program?
Yes. You start the PG Program in Data Engineering with SQL, Python and a working pipeline already in hand.
Eight weeks from now, you could be writing the SQL, not waiting for it.
SQL, Python, a pipeline that runs on a schedule on your GitHub, and placement support to put it in front of hiring teams, for ₹9,999.
Want to look first? Download the syllabus or get a call back.
Data and AI openings on WhatsApp, and the hiring market twice a month.
The roles the companies our learners work at are hiring for. 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.