r/dataengineersindia

Prior Experience - Data engineer (aws, snowflake, sql, python, gen ai)

Prior company - WITCH

Method of Application - Applied directly on career portal

There were total 4 rounds:

1. OA (30 minutes on Glider AI)

2. Technical round 1 (30 minutes)

3. Technical round 2 (30 minutes)

4. HR discussion (15 minutes)

OA:-

• MCQ questions on Glider AI platform, tech stack covered (AWS, Snowflake, DBT)

Technical Round 1:-

• Discussion in depth on work experience.
• Discussion on snowflake architecture, how does snowflake separates storage and compute.
• Discussion on Medallion architecture.
• How can we store unstructured and semi structured data in snowflake?
• Semi structured data formats in snowflake.
• Coding- Write a copy into command to load data from external stage to snowflake table.
• What are different types of stages available in snowflake?
• Coding- Write a code to create external stage pointing to a bucket in s3.
• How can we connect AWS to snowflake? explain various ways.
• Questions related to snowpipe.
• Questions related to Time travel and fail safe in snowflake.
• Coding - Write a code to get data as it was five days ago using time travel. (in both ways, offset and timestamp)
• Best ways to improve performance in snowflake.
• Explain zero copy cloning.
• Different types of storage classes available in aws s3?
• What are the different EC2 instance pricing classes for cost optimization?
• Discussion regarding to GenAI, how we are utilzing that in improving day to day activities.

Technical Round 2:-

• Design a batch processing pipeline and utilize snowflake as a data warehouse (just on high level discussion like data sources, ingestion framework, data modeling, failures, retries, cost, security etc ).
• Deep discussion on data quality and validation checks.
• SCD type 1,2.
• Coding- Write code for SCD type2 (only the main logic)
• Difference between database and data warehouse , which operation will be faster in database write or read and which operation will be faster in datawarehouse read or write?
• Normalization and denormalization.
• Dynamic table in snowflake.
• Discussion regarding to GenAI, how we are utilzing that in improving day to day activities.

HR discussion:-

• Why do you want to join Pwc and why are you leaving your current organization?
• Current ctc and salary expectation.
• Notice period.
• Benefits in PwC.
• Leaves and Insurance Plans in Pwc etc.

Verdict - Selected.

CTC offered- 13 LPA

Note : This is PwC AC guyz, kind of PwC US, so official notice period is 60 days only. In PwC India its 90 days.

Hey All,

I have close to 15 years experience in Data Engineering. I have the below 2 offers and looking for some guidance from you all on below.

1. Learning and Growth Opportunites
2. Work life balance
3. Company policies

First Offer- PepsiCo Deputy Director)
Second- Morgan Stanley VP- Finance And Risk Tech)

Hi guys, Please DM or share any company hiring now which is paying 50+ for 5+ years of experience.

My last working day is 5 Sept

My tech stack - Streaming Scala Python Snowflake AWS Gcp

Is this considered a decent salary for my experience, or am I underpaid? Would love to hear what others with similar YOE are earning

I have 5 years of work experience in data engineering .I have one offer from Cigna healthcare from bangalore location which is product based company. They are offering me 23 lpa fixed +15%variable =26.5 CTC
I have another offer from intellias(Pune) they are offering me 28 LPA fixed also when i talk to their employee. They told me that company is very good and i will get a very good profile if i work there
Also i live in pune
Now i am confuse between the offers
Pls suggest which one i should join?

Two months ago, I posted in this community after receiving my 12th interview rejection. At that point, I was honestly struggling. I had spent months studying, building projects, revising, applying, attending interviews and trying to understand where I was falling short.

The hardest part wasn't even the rejection itself. It was waking up the next day and convincing myself to start again.

I asked this community what kept people going when the effort was there but the results weren't. The responses, encouragement and personal experiences genuinely helped me.

The journey between those two posts wasn't straightforward.

There were more interviews, more rejections, recruiters who stopped responding, processes that went on hold, long waits for client feedback, rescheduled interviews and plenty of moments where I wondered:

"Am I actually improving, or am I just going in circles?"

Eventually, one opportunity worked out. So I wanted to give something back to the community that helped me when I needed it. Below are some of the actual topics/questions I encountered during my Data Engineering interviews.

This isn't a "100 questions you must know" list. These are areas and scenarios that came up during my own interview experiences.

1. The Job Search Wasn't Linear

My journey wasn't:

Prepare → Interview → Select → Offer

It was more like:

Prepare → Apply → Interview → Wait → Rejection → Analyse → Study → Apply again → Interview → Ghosting → Wait → Another interview → Rejection → Study again → Repeat.

There were several processes where I:

• Cleared one round but had to wait for the next.
• Waited for client feedback.
• Had recruiters stop responding.
• Had interviews rescheduled.
• Had processes put on hold.
• Thought I had performed well and still got rejected.
• Knew the topic but couldn't explain it properly under pressure.
• Had questions I had studied but completely forgot during the interview.

At some point, the hardest part wasn't studying. It was starting again after another rejection.

2. What I Eventually Realised

One of the biggest lessons from this process was:

Knowing something and being able to explain it during an interview are two different skills.

I had studied mainy concepts. But interviews exposed gaps in:

• Explaining concepts clearly
• Answering follow-up questions
• Writing SQL under pressure
• Connecting concepts to real projects
• Explaining why I chose a particular approach
• Remembering implementation details
• Handling questions that suddenly changed direction

So I stopped preparing by simply thinking: "I know this topic."

Instead, I started asking myself:

"What if the interviewer asks WHY?"

"What if they challenge my answer?"

"Can I explain this using a real project example?"

That made a big difference. 

🧑‍💻 SQL

Some of the areas/questions I encountered:

• WHERE vs HAVING
• GROUP BY and aggregations
• Different types of JOINs
• Customers who never placed an order
• Finding duplicate records
• Latest order for each customer
• Second-highest salary
• ROW_NUMBER() vs RANK() vs DENSE_RANK()
• LAG() / LEAD()
• Date difference / date-based analysis
• Monthly aggregations
• CTEs
• Window functions
• SQL logical execution order
• Scenario-based SQL
• Live SQL coding

Don't just practise writing the query. Be ready to explain why you chose that approach.

❄️ Snowflake

This was one of the biggest areas across my interviews:

• Snowflake architecture
• Storage vs compute
• Virtual warehouses
• Micro-partitions
• Partition pruning
• Clustering / clustering keys
• Query optimisation
• Stages
• External stages
• Storage integrations
• File formats
• COPY INTO
• Snowpipe
• Streams
• Tasks
• Streams + Tasks for incremental processing
• Time Travel
• Zero-copy cloning
• Temporary vs transient tables
• Semi-structured data
• JSON / VARIANT
• Incremental loading
• CDC
• SCD Type 2
• Snowflake operational scenarios

One interview started with basic Snowflake questions and gradually went deeper into an end-to-end scenario. So I'd strongly recommend preparing end-to-end scenarios, not just definitions.

🏗️ Data Engineering / Architecture

I encountered questions around:

• Full vs incremental load
• CDC
• SCD Type 1 vs Type 2
• Bronze/Silver/Gold architecture
• ETL vs ELT
• Batch vs incremental ingestion
• Data quality
• Failure handling
• Pipeline monitoring
• Source → ingestion → transformation → warehouse
• Fact vs dimension tables
• Star vs Snowflake schema
• Order/order-item data modelling

🐍 Python / PySpark

Topics included:

• Python fundamentals
• Lists / tuples / sets / dictionaries
• File handling
• Python for ETL
• Reading and processing files
• Data transformations
• PySpark
• Nested data
• S3-based processing
• Python scripts for data pipelines

One mistake I made was focusing heavily on Snowflake and SQL while not giving Python enough attention. If Python is on the job description, be prepared to actually write code—not just explain Python concepts.

🔄 Airflow / dbt / CI-CD

Airflow

• DAGs
• Tasks
• Dependencies
• Scheduling
• Retries
• Orchestration
• Airflow vs Snowflake Tasks

dbt

• Models
• Macros
• Testing / data quality
• Transformations
• Deployment

CI/CD

• Deployment process
• Testing
• Git
• Linux environment
• Automating data pipeline deployments

A few things I learned the hard way

1. Knowing something and explaining it in an interview are different skills.

2. Interviewers may start with a basic question and keep drilling deeper.

3. Be ready to explain everything on your resume using a real project example.

4. Don't stop applying because one company says "client feedback pending."

5. Recruiter ghosting and processes going on hold are unfortunately part of the job search. Keep your pipeline moving.

6. After every interview, write down what you couldn't answer and revise it.

7. Don't let the number of rejections become a measurement of your ability.

Finally ❤️

Two months ago, I came here asking: "What kept you going when the effort was there but the results weren't?"

At that time, I didn't have a good answer.

I think I do now.

I kept going.

Not perfectly.

Not confidently every day.

There were days I was exhausted. Days I questioned myself. Days when another rejection made me wonder whether all the studying was even making a difference.

But I kept learning. I kept applying. I kept showing up.

And eventually, something changed.

So if you're currently sitting with your 5th, 10th, 12th or 15th rejection, waiting for feedback, getting ghosted by recruiters, or wondering whether you're actually improving:

I don't know when your breakthrough will come. I can't promise that your next interview will be the one. But I can tell you this from my own experience:

Don't let your current situation convince you that it's the final chapter.

Two months ago, I was the person writing that 12th-rejection post.

Today, I'm writing this one.

Same person. Different chapter.

To everyone who commented on my original post, shared their experiences, encouraged me or simply took the time to read it:

Thank you.

Your responses genuinely helped me during a difficult period. I promised I'd come back and close the loop.

This is me doing that. ❤️

Hopefully these interview topics and experiences help someone else who is currently going through the same process.

Keep learning. Keep applying. Keep showing up. Your current rejection count is not the final number of your story. 🚀

I’m a Data Analyst with 5.4 years of experience, currently at EY. I’m confused about whether I should stay or take an offer from Carrier.

Also need to accept the offer and revert back to them by tomorrow morning first half.

If I stay at EY, I may get a promotion + ~22–25% hike and WFH. But if I don’t get promoted, the hike may only be ~10–12%.

(September end in the hike cycle)

Carrier is offering 18L fixed for a Senior Data Engineer role. They’re also willing to wait for my 90-day notice period. From what I’ve heard, hikes at Carrier are usually around 5–10%.

I’d lose my ₹2L bonus by leaving EY, but I’d recover that difference in roughly 4 months.

I also explored Deloitte, but their HR has blocked me 😭. Is there anything I can do about this or any way to approach Deloitte again?

Ps : Took a little help from AI to frame this post 😅

What would you choose in my situation — stay at EY or take the 18L fixed offer at Carrier? Also, how's the working culture at carrier?

Hey everyone,

I’m currently confused between two offers for a Data Engineering role and would really appreciate some outside perspective.

Offer A: 27.5 LPA - a Food & QSR-focused GCC
Offer B: 24 LPA - I would be working for Google as the client, based out of the Google office, but employed by a third-party company.

The main reason I’m struggling to decide is the trade-off between compensation and career exposure.

I’m trying to evaluate:

• Career growth and learning opportunities
• Technical exposure and the kind of data engineering work I might get
• Brand value on my resume
• Future opportunities after 2–3 years
• Work-life balance and culture
• How valuable it actually is to say I worked on a Google engagement when I wasn't a Google employee
• Whether the additional 3.5 LPA is worth choosing the GCC

The GCC opportunity is more attractive financially and I'd be directly part of the company's data/engineering organization. However, the Google opportunity seems potentially more valuable from a career perspective because of the exposure to Google and the possibility of working in that environment.

For people who have worked on large-company/client engagements through third-party companies: how much did that experience actually help your career later?

If you were in my position, which offer would you choose and why?

Hi leads and managers,

I am working in industry for 5 years now, and I can say that i have seen different kind of seniors, leads, peers, managers. Few were supportive, collaborative, good mentors, and approachable people but on the other hand I have seen the most disgusting people as well.

There are leads and managers who:

1. Create fake urgency of work

2. Micromanages

3. Pin points mistakes and play blame game without coming to any solution

4. Wants employees to butter them and keep them happy

5. Shouts like roadside dogs and keeps on shouting until you beg them to stop

6. Makes fun of you just to show how funny they are

And I just want to address this post to all them.

Please understand that you are working with humans, they are grown up adults with a life and something that you may not be aware of. If you are bored and need drama and spice in life, go and watch serials or focus on your own life rather than pulling others down and behaving like a roadside dog barking screaming shouting.

As a lead senior manager, learn how to deal with different people before actually handle them. Learn psychology, kindness, basic human decency.

Many might say that 'thats how corporate works', but why do you want to pass on toxicity of corporate? Why are you doing insecure to help and let other grow? Why can't you be a human first then an employee? Since decades these saga of toxicity continues and if you are in position to change it, please show empathy, kindness, practice how to listen to people, be approachable and a human first.

This world is anyway ruined with so much hatred. So, don't ruin it more. :)

For someone with 10 years of IT experience, including 5 years of relevant Data Engineering experience, what would be the bare minimum salary in WITCH companies?

Not considering Big 4 or product-based companies.

Two months ago, I posted here after receiving my 12th interview rejection(https://www.reddit.com/r/dataengineersindia/comments/1u6nkca/need\_motivation/)

At that point, I was honestly struggling. I had spent months studying, building projects, learning new technologies, preparing for interviews, and trying to understand where I was falling short. The hardest part was continuing to believe in the process when the results weren't showing.

I asked this community what kept you going during a long job search.

The responses, encouragement, advice, and personal experiences genuinely helped me. More than anything, they reminded me that rejection doesn't necessarily mean you're not improving — sometimes you just need to keep going until the right opportunity comes along.

Today, I'm very happy to come back and say:

I got a job! 🙌

The journey wasn't easy, and there were plenty of moments when I doubted myself. But I'm grateful I didn't give up.

A big thank you to everyone who commented, encouraged me, shared their experiences, or simply took the time to read my post. Your words meant more than you probably realized at the time.

For anyone currently going through rejection after rejection: keep learning, keep improving, keep applying, and don't let the number of rejections define your ability.

Sometimes one opportunity can look very different from all the applied/rejectedoppurtuinties.

Grateful for this community. ❤️

And finally, I'm happy to close the jobless chapter with a much better update.

Thank you, r/dataengineersindia**! 🚀**

Thank you

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• u/nian2326076
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I recently appeared for two technical rounds for Delloite USI
Mode : Referral

Round 1 ( 45 mins ) : Intro , Asked to explain project architecture , Pyspark code for finding highest salary among different levels/dept.
Interviewer was more interested in running the complete code , prolly to check the debugging skills.
Then some more basic questions on backfilling.

Round 2 ( 45 mins ) : Intro , Interviewer was more interested in knowing about exposure towards creating AI agents and orchestration.
Asked me to explain one of the complex scenario which I recently handled in my project and how.
Then asked a scenario based question on designing a system for order management system which would be automated by AI agents .
Data sources : PDFs into S3 and ERP.

I gave my detailed solution of creating RAG for PDFs and medallion architecture for ERP system along with glue data catalog for metadata and readme documents which AI agents can refer for taking decisions.
Then he asked some questions on CI/CD and access for users.

Feedback from interviewer for R2 : Along with technical solution , you should also focus on business understanding .

Hi everyone,
I have an NTT DATA Data Engineer Level 1 interview tomorrow for a role focused on Azure, Databricks, PySpark, and SQL.
Could anyone who has recently interviewed with NTT DATA for a Data Engineer role in India share their interview experience? I’d especially like to know:
What kind of questions are typically asked in the L1 technical round?
How much focus is there on PySpark/Spark optimization and scenario-based questions?
What level of SQL coding should I expect?
Are Databricks, Delta Lake, ADF, and Azure concepts covered in depth?
Are there any live coding or real-time pipeline troubleshooting scenarios?
Any recent interview questions, experiences, or preparation tips would be greatly appreciated.

Hey everyone

Going through the EPAM hiring process for a Senior Data Engineer role and wanted to check if anyone else has experienced something similar.

Here's my situation:

* Applied for a Senior Data Engineer role at EPAM — deployed to a top tech client in Hyderabad

* Completed two technical rounds and one managerial round, cleared all with positive feedback

* Offer discussions have already started — compensation being discussed with recruiter

* Now EPAM is scheduling an additional technical round with their internal panel tomorrow

The recruiter seemed a bit confused about the overall process when I asked for clarity — which added to my uncertainty.

My questions:

* Is it normal for EPAM to add an internal panel round after offer discussions have already begun?

* Is recruiter confusion about process stages common at EPAM?

For context — 7 years of data engineering experience with Snowflake, Spark, dbt, and Airflow.

Would really appreciate inputs from anyone who has been through EPAM's hiring process — especially for client-deployed roles in Hyderabad.

Thanks in advance 🙏

I have my first-round interview for an Amazon Business Intelligence Engineer (BIE) role this Friday. I have over three years of experience. What types of questions should I expect? Any preparation tips or recent interview experiences.

Hi everyone,

I've been feeling stuck in my current job for a while now and wanted to get some advice from people who've been in a similar situation.

I joined the company as a fresher almost 3 years ago. Initially, I worked as a Data Engineer, building data pipelines and working on actual development tasks, which helped me learn a lot.

Over the past few months, though, I've been moved almost entirely to testing. I've repeatedly asked my manager to move me back to a development project, but nothing has changed.

On top of that, my team changed last year, and ever since then I've felt the work environment has become very political. My manager is extremely diplomatic and rarely supports the team, and my tech lead almost always agrees with leadership instead of standing up for the team. It's become frustrating and has started affecting my mental health.

I've decided it's time to switch. The only challenge is that I have a 90-day notice period.

Current Profile:
- Experience: 3 years (completing this month)
- Title: Data Engineer II
- CTC: ₹14 LPA (Base)
- Notice Period: 90 days

Tech Stack:
- Snowflake
- Azure
- Python
- PySpark
- Databricks
- Apache Airflow
- Shell Scripting
- GitHub Actions (CI/CD)
- Teradata
- MySQL

I'd really appreciate advice on:

1. How should I prepare for Data Engineering interviews?
2. What topics should I focus on?
3. Is a 90-day notice period going to make switching significantly harder?
4. Which companies would be good targets with my experience?

Any guidance, interview experiences, or preparation roadmap would be greatly appreciated. Thanks!

My lwd is august 28 but not got any job offers yet. Looking for a referral for Data Engineer opportunities.

I have 3 years of professional experience and am currently transitioning into Data Engineering with hands-on learning in:

- PySpark

- Azure Data Factory (ADF)

- Databricks

- SQL

- ETL/Data Pipelines

I also have experience working in enterprise environments and am actively interviewing for Data Engineer roles.

If your company is hiring Data Engineers (Junior/Associate/Data Engineer), I would greatly appreciate a referral. Happy to share my resume and discuss my background.

Location: Chennai, India (open to relocation/hybrid/remote opportunities)

Thank you for your support!

hi guys, thoda confuse hu abhi 😅 marketing se DE me pivot krne ka soch rhi hu but technical background nhi h, isliye samajh nhi aa rha start kaha se kru.

friends trendyteck jaise paid courses suggest kr rhe h, but itna invest krne se pehle honest opinions chahiye... beginners k liye roadmap ya koi better option kya rhega?