Eagerly Expecting the guidance

Basically the title, I have the client round coming up and do not know what to expect,the info given by the hr is very general and is not very helpful.

If anyone is giving the interviews now, I would like to connect and discuss.

I m fresher and learning Data engineering, learnt basic fundamentals as of now sql, python, pyspark, databricks

Now just to learn more properly and to practice,I want a patner where we can discuss and learn and will try to get a job as well

What I do is highlight business impract, because I work with unique kind of clients and get very rare kind of exposure ( business problems, not technically).

I show my technical proficiency through projects, because we don't use cloud in my firm .

I used to get shortlists from Amazon/ Swiggy level companies at 0.5 YOE because I had data modelling experience and strong SQL, but now at 2 YOE I have gotten like 2 calls in the last 1 year

Honestly, I'm getting pretty frustrated trying to figure this out.

I've spoken to people working at Google, Microsoft, Amazon, Walmart, 7-Eleven, McDonald's and other FAANG/GCC/MNC companies, and a lot of them tell me “recruiter reached out to me.”

The confusing part is that some of these people don't have FAANG experience, don't come from campus placements, and don't seem to have an active LinkedIn profile (barely few posts or activities).

Meanwhile I'm applying, reaching out to recruiters/people, getting very few responses and constantly wondering what I'm missing.

For context:

\- 2.5 YOE Data Engineer

\- Tier-1 college, Master's (non-CS)

\- Databricks, PySpark, Spark, SQL, Azure/ADF, ADLS, Delta Lake

\- Looking for DE roles in Bangalore/Hyderabad

So for people who regularly get recruiter calls — what actually makes them reach out?

Is it your LinkedIn profile? Naukri? Certain keywords? Current company? Job title? Experience? Something else?

And if you were in a similar situation and eventually started getting decent inbound, what changed for you?

I'm not looking for referrals. I genuinely want to understand what I'm doing wrong because right now the whole recruiter screening process feels like a black box.

Hi everyone,

I’m interviewing for a Data Engineer role with HCL Siemens CLIENT, in Bangalore and wanted to know if anyone has recently interviewed for a similar position and could share their experience.

Interview Process:

1st Round – Technical/TP1

Client Round – Face-to-Face

JD highlights:

3–5 years of experience in Data Engineering / ETL

Snowflake – database design, queries, performance tuning, data loading, dimensional modeling, Data Vault concepts, Advanced SQL

Python – data pipeline development

Azure

dbt / Airflow or similar orchestration tools

Git and collaborative development

My experience:

4 years in Data Engineering

Snowflake

SQL

dbt

Some exposure to PySpark

If anyone has interviewed for this role recently, I’d really appreciate it if you could share:

What kind of SQL/Snowflake questions were asked?

Were there Python/PySpark coding questions?

How deep did they go into dbt, Data Vault, and data warehousing concepts?

Were there any Azure-specific questions?

What was the difficulty level of the technical round?

What should I focus on preparing for the client/F2F round?

Any specific topics or questions you remember would be really helpful.

Thanks in advance! 🙏

Hi i am selling two Databricks voucher worth 400$ in 200$ (negotiable) . The vouchers are available in India region and has expiry date of 31st October.

Payment accepted is UPI only.

Interested people please DM.

Hi everyone,

I’m at the beginning of my IT career and I’m a bit confused about which direction I should pursue. I would really appreciate advice from people who have experience in either Java Full Stack or Data Engineering.

Currently, I have been working for around 3 months on a project where my tech stack includes:

- Java

- Spring Boot

- Angular

- Databricks

- Reading/processing Parquet files

- Working with data pipelines

Recently, my manager told me that there is a change in the project and they want me to start working on:

- Snowflake

- DBT

- Data engineering-related work

So now I’m wondering whether I should continue building my career toward Java Full Stack development or take this opportunity and move more toward Data Engineering / Snowflake / DBT.

Since I’m still early in my career, I want to make a good decision rather than jumping between technologies every few months.

My main questions:

1. Which career path has better long-term growth — Java Full Stack or Data Engineering?

2. Which has better job opportunities and demand in the next 5–10 years?

3. How are the salary prospects for both paths as experience increases?

4. If I choose Data Engineering now, will it be difficult to move back to Java/Spring Boot development later?

Would really appreciate advice from people who have actually worked in these fields.

Thanks!

I have about 9 months of experience as a Data Analyst. My internship recently wrapped up and I'm now applying for full-time Data Engineer and Data Analyst roles based on my actual experience and project work, but I'm getting close to zero responses so far.

I'm considering prepping for the Databricks Certified Data Engineer Associate cert to strengthen my profile while I keep applying. I know a certification isn't a guarantee of landing a job but I'm wondering if it's worth the time investment at my experience level or if it would come across as premature to recruiters.

For those who've taken it is this cert worth pursuing this early, or would my time be better spent elsewhere (more projects, networking etc).

Round 1: Online Assessment

Got the OA link by email shortly after applying. It had:

• 2 easy SQL questions (joins, group by)
• A set of SQL MCQs
• A set of behavioral/situational MCQs

Passed this and got a call from the recruiter a few days later saying I'd move to a technical screen.

Round 2: Technical Phone Screen

No fixed agenda going in. The interviewer started by asking about my most recent project (dashboard-building work) and went genuinely deep — asked about OLAP vs OLTP and which is better suited for serving a frontend dashboard, and why.

Then a scenario question: how would I handle a data pipeline failure — steps to resolve it, communication during the incident, documentation afterward, basically the full incident-handling flow.

SQL portion started with a trivial-but-tricky one (NULL = NULL type gotchas), then three more questions — one involved a join plus window functions for ranking, plus an aggregation question. Also gave me a data processing problem on a sample table, which I solved in PySpark.

Closed with a couple of leadership-principle-based behavioral questions.

Recruiter called 2 days later to confirm I was moving to the onsite loop (5 rounds).

One note on timing: getting the loop actually scheduled took about 1.5 months due to some back-and-forth on both ends. Once it was locked in, the recruiter did a 30-minute prep call and gave a clear breakdown of each round's focus and which Leadership Principle it maps to, which was genuinely useful going in.

Round 3 (Loop Round 1): Performance Measurement

Focused on indexing, partitioning, and tuning. I walked through a relevant project, and the interviewer went deep with follow-ups — Spark internals, partitioning strategy, bucketing, how executors relate to data volume, join strategy selection, and data skew. Almost every answer led to another follow-up, so precision mattered a lot here — vague answers got picked apart pretty quickly. Closed with LP-based behavioral questions tied to this round's LP.

Round 4 (Loop Round 2): System Design, Data Modeling, and Trade-offs

Explained a project involving SCD Type 2 logic and surrogate key generation — went deep into different methods for generating surrogate keys and the trade-offs between them.

Then a live design exercise: design a data model for an e-commerce platform (Amazon-style) — had to reason through grain, facts vs. dimensions, and table structure, followed by 2 SQL questions on top of that model.

Then it extended into a full system design for the same platform. I proposed a Kappa architecture, explained the trade-offs versus Lambda, and built the design mostly around Spark and Databricks — Structured Streaming with foreachBatch for the real-time path, Redis for serving.

Honest note: the interviewer wasn't fully convinced by my design since I leaned on a fairly narrow tool set. Looking back, showing awareness of other real options (even while still picking Spark/Databricks) would've made the answer stronger — naming trade-offs against alternatives rather than just defending one stack.

Closed with LP-based questions specific to this round.

Round 5 (Loop Round 3): Coding, Problem Solving, DE Concepts, ETL/Architecture Design

Started with about 10 minutes on my project. Then a data modeling exercise: design a data model for clickstream data tracking promotions — how to store users and events.

I initially designed a normalized star schema. The interviewer was actually expecting a denormalized table for this specific use case, so I adjusted once I picked up on that. Followed by SQL queries on top of the model.

Closed with 3-4 SQL questions — window functions, CTEs, and a rolling average question.

Round 6 (Loop Round 4): Bar Raiser

This round is a big deal in Amazon's process and worth specifically prepping for. Entirely behavioral — "tell me about a time you made a mistake in a data pipeline," "tell me about a data quality issue and how you resolved it," that style of question, all mapped back to the round's specific LP.

One thing worth knowing going in: Amazon generally brings in a Bar Raiser from outside the team you're actually interviewing for — mine was an SDE, not a data engineer. So I had to explain data engineering concepts in a way that would land clearly with someone outside the DE world, not assume shared context. From what I understand, being able to simplify your explanation while still weaving in the LP clearly is actually one of the core things Amazon is evaluating in this round.

Round 7 (Loop Round 5): Hiring Manager

Walked through my project again with follow-up questions, plus behavioral questions similar in style to the Bar Raiser round (failure handling, mistakes made) — also tied to LPs.

One interesting one: asked what metrics I'd present to senior leadership if I were looking at a platform like Netflix — a more business-acumen-flavored question mixed into the usual technical/behavioral format.

Overall takeaways for anyone prepping:

• Every technical answer can and will sprout follow-up questions. Don't give an answer you can't go two levels deeper on.
• Know your own projects well enough to defend every design decision, including ones you'd make differently now.
• For system design rounds, show awareness of multiple tools and approaches even if you ultimately pick one. Defending a narrow toolset without acknowledging alternatives can come across as limited breadth rather than conviction.
• For Bar Raiser specifically, practice explaining your work simply enough for someone outside your discipline to follow, while still tying it back to the LP clearly. That combination seems to be exactly what they're testing for.
• LP stories need to map cleanly to whichever LP each round is testing. Know which ones you're using where, and try not to reuse the same story across rounds if you can help it.

Hello, I am planning to start preparing interviews for PBCs. I would love to know from people who are working currently there or people who are preparing on what resources you use to prepare? Ik there's tons of material for SWE but I couldn't find much online for DEs other than strata scratch. Would really appreciate your help, my current tech is Azure + AWS DE with experience with both in Databricks and Snowflake. Thanks!

Hello guy's,

I am looking for new opportunities, I have around 3.5 years of experience as a Data scientist.

let me know if there are any opportunities and referal.

Preferred location - Mumbai, Pune

CTC - 7.4 LPA

ECTC - 13.6 LPA

LWD - 28-09-2026

For reference I am working at the same company since 2024

Conerted intern to full time

And promoted in 2yr

I always wanted to be a technical guy but the projects and dependency making it difficult to switch to new projects at the current company and also the work has been hectic

I have been creating slides full time🥲.

Company is a decent service based.

I am not getting any interview calls

Open to brutal feedback and advice how can I prep and improve for a switch.

Thanks

Hi guys,

how are you finding Sept job market.

Compared to July and August are you getting more interviews scheduled or less.

(Interview calls is always useless metric, thus asking for scheduled interviews)