Python not required on day one. Build the data AI runs on.
Become a Data Engineer in 34 weeks: build a production data platform on AWS, Azure or Google Cloud, live on weekends.
A data engineering course with placement support and real-time projects, for working professionals: weekends, live, with recordings.
Taught live by industry experts from Mastercard and Paytm.
Download the syllabusBook a 1-on-1 call with a program advisor
Every weekend live, every class recorded and up within 24 hours.
An end-to-end project, four project weekends, and twelve more along the way.
AWS, Azure or Google Cloud, each with a certification route.
From week 20, with industry experts who have hired for the role.
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 this data engineering program is for.
Working professionals who want the Data Engineer title, and the systems to back it up.
Software engineer→ Data engineer
You already ship code. This program adds the data layer, the cloud and the title.
Support, testing or ops→ Data engineer
Production is already familiar ground. Here, the pipelines that run on it.
Analyst→ Engineer
Data every day, and a queue of pipeline requests. Here, the pipelines get built instead of waited on.
A career break→ The next role
A gap changes nothing here. The program is about the next role, and it builds the route to it.
- A bachelor's degree in any stream
- Weekends: six hours live, Sat and Sun 10 AM–1 PM, plus 4–5 hours of practice recommended
- A laptop with an i7 or Ryzen 7 and 16 GB memory
- Your own cloud account, set up before the cloud weeks; free tiers cover most of the program
Data Engineering Fundamentals: an eight-week head start.
The first eight weeks of this program, sold on their own: AI, SQL, Python and Git from a standing start, ending in the Kirana Ledger pipeline on GitHub. Your certificate ID carries into this program.
Pick AWS, Azure or Google Cloud at the start, and every cloud project lands on it.
Data engineering is the same discipline on all three clouds; only the service names change. Choose once, and every project lands on that platform: eight months of working as an AWS, Azure or Google Cloud data engineer.
AWS
S3 · Lambda · Glue · Athena · Redshift · EMR · MWAA · Kinesis
Certification routeAWS Certified Data Engineer – Associate
Microsoft Azure
ADLS · Functions · Microsoft Fabric · Azure Databricks · Fabric Airflow jobs · Event Hubs
Certification routeMicrosoft Certified: Fabric Data Engineer Associate
Google Cloud
Cloud Storage · Functions · Dataflow · BigQuery · Dataproc · Composer · Pub/Sub
Certification routeGoogle Professional Data Engineer
Week 13 maps your build onto the other two clouds, because the next job may use a different one.
Code it. Deploy it. Break it. Fix it.
The full data engineering course syllabus: 34 weeks in four phases, four project weekends, and the job title each block opens as you reach it. Every cloud project lands on the cloud you pick. Open any phase for what gets built and the tools used.
Foundationsweeks 1–8 · 8 weeks
- W1–2AI Launchpad: how AI works, and the assistantsHow LLMs work · ChatGPT, Claude, Gemini and Copilot, and how to prompt them · checking the answer · a first AI workflow2 wks
- W3–4SQL for dataJoins · GROUP BY · CTEs · window functions · reading a query plan · a timed SQL set2 wks
- W5–6Python for dataFunctions and errors · pytest · APIs and JSON · pandas · NumPy · a tested cleaning script2 wks
- W7Linux, Git and an AI coding assistantbash · permissions · cron · Git branches and pull requests · an AI assistant inside a real repo1 wk
- W8Project weekend 1 · Kirana LedgerYour first pipeline: database to clean table, on a schedule, built live and graded1 wk
Cloud & scaleweeks 9–20 · 12 weeksOpensBig Data Engineer · Data Warehouse Engineer · AWS / Azure / GCP Data Engineer
- W9–10Data foundations and modellingLake, warehouse, lakehouse · Parquet and Avro · partitioning · facts and dimensions · slowly changing dimensions · MongoDB hands-on2 wks
- W11–13Cloud core · your trackIAM · object storage as a lake · serverless ingest · managed ETL · the cloud warehouse · secrets, budgets and cost alertsAWS / Azure / GCP Data Engineer3 wks
- W14Project weekend 2 · Highway Toll Data PipelineSource to lake to warehouse to dashboard, on your cloud, built live and graded1 wk
- W15–17Spark and DatabricksSpark architecture · joins and shuffles · skew and AQE · the Spark UI · Delta Lake and Iceberg · Unity Catalog · a Databricks jobBig Data Engineer3 wks
- W18–19Snowflake, and what it costsLoading and Snowpipe · clustering · time travel · roles and access · semi-structured data · a monthly cost modelData Warehouse Engineer2 wks
- W20Project weekend 3 · Festival Sales Spark PipelineSpark on Databricks into Snowflake, built live and graded1 wk
Production & AIweeks 21–31 · 11 weeksOpensETL / ELT Developer · Streaming Data Engineer · Data Integration Engineer · Analytics Engineer · Data Quality Engineer · Data Engineer · Data Platform Engineer · DataOps Engineer · AI Data Engineer
- W21–22Pipelines on a schedule · your trackAirflow DAGs, sensors and retries · Docker · alerting · managed Airflow on your cloud · the Mandi Price PipelineETL / ELT Developer2 wks
- W23–24Data in motion · your trackKafka topics and consumers · Schema Registry · delivery guarantees · event time and windows · Spark Structured StreamingStreaming Data Engineer · Data Integration Engineer2 wks
- W25Project weekend 4 · Real-Time Payment MonitorA live payment stream with a running total and an anomaly flag, built live and graded1 wk
- W26–27Analytics engineering and data qualitydbt models and materialisations · incremental models and snapshots · tests and freshness · lineage · a data quality test suiteAnalytics Engineer · Data Quality Engineer2 wks
- W28–29Production, governance and DataOpsMonitoring, runbooks and root cause · PII masking and DPDP · data contracts · catalog and lineage · CI/CD · TerraformData Engineer · Data Platform Engineer · DataOps Engineer2 wks
- W30–31The data layer under AILLM calls inside a pipeline · training-data pipelines and feature stores · embeddings and vector stores · RAG ingestion · MLflowAI Data Engineer2 wks
Career & end-to-end projectweeks 32–34 · 3 weeks
- W32Hiring weekData-platform system design · timed SQL, Python and DSA-for-data rounds · your portfolio page · cloud certification routes1 wk
- W33–34Capstone: the end-to-end projectBatch and real time in one platform · costed and governed · presented before a panel2 wks
The end-to-end project · PG Program in Data Engineering
Batch and real time in one platform · costed to the rupee · presented live to a panel
The detailed curriculum, the week-by-week timetable, every tool and the faculty, in one file.
- Every week and every topic
- The timetable, with dates
- Every tool, by phase
- The faculty who teach it
Download it now. A program advisor calls within one working day to walk through it and answer anything it leaves open.
Your syllabus is ready.
A program advisor calls within one working day to walk through it.
Taught live by industry experts who build and teach these systems.
Taught live on weekends, with doubts answered in the weekday channel and the Saturday doubt clinic.
Try a free live build, and meet the faculty before you apply.
Ninety minutes, live and free. Our industry experts build a working AI tool from scratch while you build it alongside, chat open. It's the clearest way to see how a session here works. Next sessions: Sat, 17 Oct 2026 or Sat, 24 Oct 2026, 11:00 IST.
Your seat is saved.
The joining link reaches you on WhatsApp, with a reminder on the day.
Three things that make thirty-four weeks work.
Hands-on, from week one
Every block ends in something built and reviewed by the faculty, and four project weekends to finish and catch up.
The tools companies hire for
Spark, Databricks, Airflow, Kafka, dbt, Snowflake and the rest of the stack, on AWS, Azure or Google Cloud, with a certification route for each.
Built around a full-time job
Live on weekends, every class recorded and up within 24 hours.
What a week looks like.
Everyone moves through the same week on the same deadline, so a bad week costs you a replay, not the cohort.
- 01The brief, firstWhat you'll build this week, and what done looks like. Before the session, not after.
- 02Build it liveOn screen with the industry expert teaching it, chat open, your question answered on the call.
- 03Finish itStarted in the room, finished by the deadline, on your cloud account.
- 04Feedback, weeklyFrom the faculty every week. Marks come only when a test or project is due.
- 05Bring what brokeThe Saturday doubt clinic, 3–4 PM, and a weekday doubt channel, for the thing that won't run.
- 06It carries forwardEvery block hands its work to the next. Nothing built is thrown away.
A data engineering course with real-time projects: one end-to-end data platform, and the projects that build up to it.
Each runs on a personal cloud account, and each is finished when it does the job it would do in production. An interviewer can open it and run it.
Your own data platform, presented live to a hiring panel.
Batch and real time in one platform, with the AI layer on top and a monthly running-cost statement, presented to a panel that has hired for this role.
- How it works
- From week 11 the data moves on your cloud. From week 23 it moves in real time. By week 31 it feeds an AI assistant: chunked, embedded, indexed, scheduled.
- Where it is used
- The same shape runs a bank’s payments reporting, a retailer’s daily sales, a delivery app’s live tracking, and the data behind an AI assistant.
- What it proves
- That you can build, run, cost and explain a data platform. Every piece of it can be opened in an interview.
Projects along the way
Yours depend on where you start and the track you pick. Everything leads into the end-to-end project. Some examples:
Kirana Ledger
A shop's daily sales moved from a database into a clean, tested table every night: your first pipeline, built from a standing start.
Proves you can write code a team can read, test and schedule.
Highway Toll Data Pipeline
Toll records moved from source to lake to warehouse to dashboard, on a schedule, on your cloud.
Proves you can build a cloud pipeline that runs end to end, unattended.
→Festival Sales Spark Pipeline
Festival-season sales too big for pandas, processed in Spark on Databricks as partitioned Delta tables, then into Snowflake.
Proves you can make a slow job fast, and explain the speed-up.
→Market (Mandi) Price Pipeline
Crop prices loaded into a warehouse every night, checked before loading, with a Slack alert when a night fails.
Proves you can rerun any night without double-counting a row.
→Real-Time Payment Monitor
A live stream of payments with a running total and an anomaly flag.
Proves you can keep a stream correct when a message arrives twice.
→Data Quality Test Suite
Tables broken on purpose, and a test suite that catches every fault.
Proves you can stop a bad load before anyone reads it.
→Data Pipeline for an AI Assistant
The ingestion pipeline behind an AI assistant: documents chunked, embedded and indexed on a schedule.
Proves you can build the data layer an AI product stands on.
→Every project is reviewed by the faculty who teach it.
Six lines every data engineer’s resume needs. Yours will have all six.
Data Engineer · AWS
Projects
- End-to-end data platform batch and real time in one platform, costed to the rupee, presented to a hiring panel
- Highway Toll Data Pipeline source to lake to warehouse to dashboard, unattended for seven days
- Market (Mandi) Price Pipeline nightly Airflow runs, rerun-safe: 128 rows before, 128 after
Technical skills
- Ingestion batch and streaming pipelines, source to lake
- Storage & modeling lakes, warehouses, dimensional models
- Processing at scale Spark, distributed compute, tuning
- Orchestration scheduled, monitored, recoverable pipelines
- Cloud one platform end to end, the other two mapped
- Quality & production tests, lineage, runbooks, cost
See what your resume is missing for a data engineering role.
Three fields open it. Then upload your resume or answer four questions. Your skill report arrives within 10 minutes: the lines already there, the ones this program adds, and the closest title today.
Your analysis is on its way.
A program advisor calls within one working day to walk through it.
Placement support: five steps to your first data engineering job.
The resume is the raw material. From hiring week to the first offer, this is the work done on it, step by step.
- 1Week 32The whiteboard roundsData-platform system design, timed, with feedback, plus your portfolio page and the README a hiring manager reads first.
- 2End-to-end projectThe panelThe platform, presented to people who have hired for this role, with their notes added to the portfolio.
- 33+ roundsThe mock interviewsFrom week 20, with industry experts who have hired for this role. Recorded, with feedback, and repeated until ready.
- 43+ reviewsThe portfolio reviewEvery project checked against what an interviewer opens first, before any application goes out.
- 53+ introductionsThe introductionsWarm introductions into the 39+ companies our learners work at, a job board of the roles they're hiring for, and support that continues [support window to come].
Our learners work at 39+ companies.
The introductions in step five go into this network.
amazon
Swiggy
Twitter
ByteDance
PayPal
Flipkart
ATLASSIAN
Training a team? 12+ organizations bring us in to train their own engineers, on their own stack. 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 program builds it.
From where they were to where they are.
The clinic is what got me through the Spark weeks. I brought a Spark job that had been broken for a week, and the faculty sat with me until it ran.
I came from support. I already knew what breaking at 2 am felt like; this is where I learned to build the thing that doesn't. The runbook from the production weeks is the one I still use.
Four years out, and the end-to-end project panel was the first interview I'd done in all that time. They took the platform apart for forty minutes. The real interview two weeks later felt short.
Two data engineering certificates an employer can verify in ten seconds.
Two certificates for shipped work, not attendance, each with a verification ID an employer can check.
Awarded by SkilD on completion. What each certifies, and the bar that earns it, is stated in full on enrolment. Your track also maps onto its cloud certification, AWS, Azure or Google.
One fee for the data engineering course, everything included.
From ₹3,516 a month on EMI, GST included.
- Program fee₹1,14,000
- Phase discountsApplicable
The fee follows the admission phase. Phase 2 closes 11 Oct 2026; the fee rises when Phase 3 opens on 12 Oct 2026.
30 seats. When the cohort fills, it closes and the next dated cohort opens.
- All 34 weeks, taught live by industry experts
- The end-to-end project, and every project along the way
- Cloud credits for your approved projects
- 3+ mock interviews
- Portfolio reviews and introductions
- Your cloud certification route
All amounts include GST. Students, people coming back from a break and women in tech: ask about financial aid on your 1-on-1 call.
Why 34 weeks, and not three months?
A short course teaches the tools. Employers hire the person who has run a platform, broken it, fixed it and explained the bill. That takes eight months of weekends.
| The route | What it usually gets you | What 34 weeks here gets you |
|---|---|---|
| YouTube and free courses | Everything, in the wrong order, and nobody to ask when a pipeline breaks at 11 pm. | A fixed sequence, and the industry expert teaching it answering in the session. Start with Data Engineering Fundamentals |
| Self-study on your own cloud | One thing built well, and no way to know what was missed. | An end-to-end platform and the projects that build up to it, each finished to a standard set by industry experts who run these systems. |
| A master's or a diploma | Two years, a far larger fee, and a syllabus written before this stack existed. | Eight months of weekends, on the tools companies are hiring for this quarter. Fee a stretch? Students, people coming back from a break and women in tech can ask about financial aid |
| A large-batch course | A crowded room, a recording, a support ticket. | A room of thirty, an industry expert teaching it live, your name known by week three. |
From your application to your first live session.
- 1ApplyTen minutes, right here.
- 2Eligibility checkA bachelor’s degree in any stream, and the right starting point for you.
- 31-on-1 call with a program advisorWithin one working day: the timetable, the faculty and the fee.
- 4Reserve your seatHolds your seat and locks this phase’s fee.
- 5Join the cohort WhatsAppYour confirmations and the cohort’s updates arrive here.
- 6Call with your Student Success ManagerYour plan, your cloud account and your first weeks, set up.
- 7Recordings and setupThe SQL and Python recordings open on enrolment; a live setup session runs the Wednesday before week 1.
- 8First live session28 Nov 2026.
- One price, everything includedevery project, every clinic, every mock interview.
- 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.
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.
Aiming at something next to data engineering? Take the fifteen-minute skill check or see the other programs, and we'll map the route with you.
Data engineering course: your questions, answered.
What is a data engineer's salary in India?
Up to ₹80 LPA for Data Engineers (recrew.ai, 2026), up to ₹90 LPA for Data Platform Engineers (PlugScale GCC benchmark, 2026) and up to ₹73 LPA for Analytics Engineers (6figr). Engineers who can run pipelines in production earn 20–35% more than generalists, and the highest salary hike a SkilD learner has recorded is 82%.
What does an AWS data engineer do all day?
Lands source data in S3, models it, moves it into Redshift, schedules the whole thing in Airflow, and gets paged when it breaks. Weeks 11–13 are that job, weeks 21–22 the scheduling, and weeks 28–29 the pager.
What is the difference between a data engineer and an analytics engineer?
A data engineer moves and lands the data. An analytics engineer makes it trustworthy and documented for everyone who uses it. Weeks 26–27 are the analytics engineering half, which is why both titles are on the roles list above.
I've done data engineering courses before and still can't build anything end to end. What's different here?
An end-to-end data platform and the projects that build up to it, on a personal cloud account: not exercises, but systems to open in an interview and walk someone through.
Can I learn data engineering with no coding background?
Yes. Weeks 3 to 7 assume none: SQL and Python from first principles, a short basics check before week 5, and Linux, bash and Git by week seven. To build that base first, Data Engineering Fundamentals is eight weeks and leads into this program.
Can I move into data engineering from support, testing or ops?
It's one of the fastest routes in. Production, on-call and things breaking at night are already familiar, which is the half most career switchers are missing. The Linux week and the production weeks will feel like home; modelling, Spark and dbt will stretch.
Which cloud should I choose for data engineering?
The one target employers use. The setup session reads live job listings for the roles in view, and week 13 maps the build onto the other two, so the choice never narrows the options.
Do I need my own cloud account for the data engineering course?
Yes, set up before the cloud weeks. Free tiers cover most of the program, and the cost weeks (13 and 19) and the end-to-end project teach reading and controlling the bill, which interviewers ask about.
Can I do this data engineering course while working full time?
It's built for people with jobs: six hours a week live on weekends, Saturday and Sunday 10 AM–1 PM, with 4–5 hours of practice a week recommended, and every class recorded and up within 24 hours. The cohort moves together on a fixed schedule, which is why people finish.
What are the data engineering course fees?
The program fee is ₹1,14,000 including GST. Phase discounts apply while a phase is open. It covers all 34 weeks, the end-to-end project and every project along the way, cloud credits, mock interviews and placement support. EMI from ₹3,516 a month.
Can I pay the data engineering course fee in EMIs?
Yes, from ₹3,516 a month. The provider and terms come with your application.
What data engineering certification do I get?
A SkilD certificate on completion, with a verification ID, plus a mapped route to your track's cloud certification (AWS, Azure or Google), so the 34 weeks feed straight into a credential employers already recognize.
I can follow a tutorial but I freeze in data engineering interviews. Does this course fix that?
Yes: timed whiteboard sessions, a technical question bank, and mock interviews with people who have hired for this role.
Is a data engineering job guaranteed after the course?
The offer is won in the interview, and everything here is built so you walk in ready: hiring week, the end-to-end project reviewed by a panel that has hired for this role, a published portfolio, mock interviews, the question bank, and introductions into the 39+ companies our learners work at.
Do you run data engineering training for companies?
Yes. We run this program for teams, built around the team's stack and data. Plan a program for your team →
Most people will watch the AI boom. Join the ones building it.
Thirty-four weeks from now, this could be your job: a data platform built, a portfolio worth walking someone through, one cloud known properly, and a market that pays up to ₹80 LPA for exactly this work.
Not ready to apply yet? 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.
ChatGPT · Python · PostgreSQL · Git
AWS · Azure · GCP · Spark · Databricks · Snowflake
Airflow · Docker · Kafka · dbt · Terraform · LangChain
✓ file landed · 01:58
✓ 128 rows · 0 nulls in keys
✓ prices joined to markets
✓ loaded · 128 before, 128 after
Priya
Kabir
Meera
Rohit
Ananya
Imran