Has anyone recently attended lead data engineer interview at JP Morgan? Would like to know what kind of questions are asked here.

Seeking full-time, part-time, or contract opportunities as a Data Engineer. I have 3 years hands-on experience with Python, SQL, PySpark, Databricks, Delta lake and Azure through real-world data engineering projects and am currently learning DevOps for Data Engineering. Referrals and opportunities are greatly appreciated.

I’m currently trying to transition deeper into Data Engineering and would really appreciate some perspective from people who are already working in the field.

I have 1.3 yrs experience as a Junior Python Developer. What I want to do is slowly transform into a Data Engineer. How would you suggest my choice? Basically what I do is make web scraping scripts to get the data from web and give the data in excels. Our company is currently not using git or CI/CD or anything like that. 

The problem I’m running into is that when I look at Data Engineering jobs on Naukri, LinkedIn, etc., the requirements seem endless.

One job asks for Python, SQL, Airflow and AWS; another wants Spark, Kafka and Databricks; another wants Snowflake, dbt, Terraform, Kubernetes, CI/CD, etc. It becomes difficult to understand what I should actually prioritize.

So I’d like to hear from people who are actually working as Data Engineers. What does your day-to-day work look like? What kind of problems do you solve, what technologies do you use regularly, and which skills have turned out to be genuinely important in your job?

More importantly, based on my current experience, what would you suggest I improve or learn next to become a stronger candidate for Data Engineering roles? Are there any gaps that you think I should focus on, or technologies/concepts that are worth learning through projects rather than just studying theoretically?

I’m more interested in understanding the reality of the job and getting advice from people who have actually gone through the transition.

Thank You

Guys I tried uploading the first set of notes, ended up using the Oken scanner as suggested by one of the fellow community members:

Uploaded notes for gcp (first 50 pages of my notes) - https://drive.google.com/drive/folders/1xMGLLGqci_D4F9JSXPTFto6RJAQORb7g

Pls let me know if they are helpful or not so legible/not helpful will continue upload on this link if it's of help, if not then apologies for the inconvenience.

Hey all, looking for some honest feedback and a reality check.

I've applied to somewhere between 30-50 companies over the last month (mostly Data Engineer roles at product companies) and I'm getting almost nothing back. A handful of straight rejections, but mostly just silence. No calls, no OAs, nothing.

I'm trying to figure out if this is:

a resume problem,

an application-channel problem (I've been applying mostly through LinkedIn and Naukri),

just the current market for ~2 YOE data folks, or

something else entirely.

I have attached my resume and would really appreciate your blunt feedback on the resume itself, formatting, bullet content, anything that reads as filler or unclear.

Any platforms/channels beyond LinkedIn and Naukri that have actually worked for you especially for product company Data Engineer roles.

Suggest me some improvements in my resume.

At this point, the way databricks is evolving is honestly getting hard to keep up with.

A feature you worked with last month might have already gone through major updates, and you wouldn’t even know about them unless you’re constantly keeping yourself up to date.

TL;DR: Been using Databricks SDP the number of updates and changes it has gone through in just the past few months is crazy. Don’t know why, but I really felt this while working.

I recently joined Cognizant and have been onboarded. I was told my role will be mainly L2 support for Databricks and Snowflake.

My long-term goal is to become a strong Azure Data Engineer / Databricks Engineer.

My current situation:

Strong in SQL and reasonably comfortable with PySpark
Good theoretical understanding of Databricks, Spark, Delta Lake, ADF, etc.

Limited real-world production experience with Databricks
I’ve struggled in some client interviews when they ask deep production/scenario-based questions
I want to build genuine hands-on experience rather than just prepare theoretically
I’m wondering whether L2 support is a good way to bridge that gap.

For people who have worked in Databricks/Snowflake L2 support:
How technically challenging was the work?

Did you actually troubleshoot Spark/Databricks jobs, SQL issues, performance problems, data quality issues, pipeline failures, etc.?

Did you get opportunities to modify/fix PySpark or SQL code?

Were you involved in RCA and permanent fixes, or mostly monitoring/restarting jobs/escalating tickets?

Does this kind of experience help when applying for Data Engineer/Databricks Engineer roles later?

Would you choose L2 Databricks/Snowflake support over waiting for a pure development project if you were in my situation?

I’m particularly interested in hearing from people who started without much production experience and used support work to move into development.

Would you consider this a good stepping stone, or should I try to move toward a development project instead?

Hi Everyone,

I have 4yrs of experience in IT,Recently I got placed in a MNC company that pays me around 12+Lpa for data engineer role. Though I have knowledge in pyspark,databricks, Sql .I don't think I can crack any product based companies ,heck my profile won't even get shortlisted. I have all the time to prepare and learn but don't know where to start what to even focus on,if you do have the same skillset and you are in a learning phase please DM so we can help each other out on what we know or if you are already in a higher package for this role like 20+LPA please guide me what I should focus more on ,any learning materials would be helpful .Thanks

i want to start my cloud for Data engineer in Azure. so i went to `Youtube` and i found this resource here so if you have experience with this playlist or are you currently an Azure Data Engineer then please help me to find some good resource to learn Azure for Data Engineer

i know i don't have to learn whole Azure ecosystem but For DE roles is this playlist is good or not here its also pretty old (3 year old ) so is it still relevent please guide me here for this and if you have better resource please share it here for DE

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Hi everyone!

We’re looking to hire Data Engineers (3YOE and above) at PwC India to work on AI/ML use cases.

Tech stack: AWS / Azure

Looking for experience with:

- Data Engineering / Data Pipelines

- Cloud platforms (AWS or Azure)

- Azure Databricks

- Databricks Genie

- AWS Greengrass

- AWS Lambda

- Amazon Redshift

Experience with some of the above technologies is a plus — you don't necessarily need to have worked with all of them.

If you’re interested, please DM me your resume along with your current location and experience level.

Thanks!

Hey everyone,

I’m currently serving my notice period and actively looking for Data Engineering opportunities. My last working day is 25th September 2026, and I can join immediately as per the requirement.

Experience: 3 years

Skills: Databricks, PySpark, SQL, AWS, Snowflake, Azure

Notice Period: Currently serving | LWD: 25th September 2026 (can join immediately)

Availability: Immediate joiner

I’m primarily looking for roles involving Data Engineering, Databricks, PySpark, AWS/Azure, and cloud-based data platforms.

If your organization has any relevant openings and you’d be comfortable referring me, I’d really appreciate your help.

Happy to share my resume and additional details via DM.

Thanks in advance who reads this or helps with a referral.

Hey everyone,

My qualifications:

• Electronics & Telecommunication Engineering
• 2024 graduate
• 8.2 CGPA
• ~1.8 years of experience as an Analyst

I joined an MNC after getting an offer of 3 LPA for an Analyst position. The job was close to home, general shift (8:30–4:30), and seemed like a decent opportunity, so I accepted it.

After joining, I found out that the actual role was more like Safety Officer + Lean Analyst. The analyst side involved a lot of Excel, reporting, basic SQL, process improvement, etc.

Initially, things were fine, but over time the on-ground environment became extremely toxic. As the safety person, I constantly had the pressure of knowing that if something went wrong, I could potentially be held responsible. There were also conflicts with some people on the manufacturing side, including fights and baseless complaints.

The positive side was that I worked on several improvement projects and had direct interaction with global managers based in Germany, Spain, Poland, Sri Lanka, etc. My manager and the GM were also supportive and understood the situation.

Eventually, though, the safety responsibilities became too much and I decided to resign.

My original plan after resigning was actually quite straightforward: take a short break, then spend a few months systematically learning Python, SQL, ETL, data engineering, Databricks, etc., while applying for jobs and preparing for a Master's in Germany.

Unfortunately, things didn't go as planned.

My health took a turn for the worse, with severe digestive/reflux problems that were heavily affected by stress. Because of that, I had to put my Germany Master's plan on hold as well. From around April to August, I was mostly at home focusing on getting better and didn't make much progress professionally.

Now I have a ~5-month career break, and I'm honestly confused about what to do next.

I'm genuinely interested in Data Engineering, but I feel stuck between two options:

1. Start learning Data Engineering properly. This could take another 6–8 months before I'm job-ready, which could leave me with a ~1–1.5 year career gap.
2. Take any analyst/IT job now just to get back into the workforce, and learn Data Engineering alongside it.

The problem with option 1 is that there is obviously no guarantee I'll get a job after spending those months learning.

I'm also worried that recruiters will look at the career gap and question why I left my previous job.

So if you were in my position, what would you actually do?

Would you:

• Target Analyst/BI/Data Analyst roles first after 3 months of preparation and transition into Data Engineering later?
• Directly prepare for junior Data Engineering roles?
• Take a non-data job temporarily and keep learning on the side?
• Or do something completely different? I have my

I know I made some mistakes in how I handled the transition, but I don't want to keep sitting at home and make the gap even bigger.

I'd really appreciate practical advice from people who have actually made a transition into Data Engineering, especially from a non-CS/Electronics background.

Hey

My company hiring for experienced data engineers

Share your resume and this is mostly onsite role at Surat location and also company provide remote roles as per experience and discussion with HR team.

I’m exploring freelance/contract Data Engineering opportunities, with availability of 15–20 hours per week.

I have 13+ years of experience working with leading investment firms and financial institutions(jpmorgan, wells fargo, dtcc), specializing in:

• Data Engineering & Architecture
• Lakehouse & Data Mesh
• AWS, Snowflake & Databricks
• Spark / PySpark, Kafka, Python & Java
• Large-scale data platforms and modernization

Open to remote, part-time, or project-based engagements across the US, UK, UAE & Middle East.

If you know of any relevant opportunities, please DM me. Referrals are highly appreciated!

I recently left Accenture, and my new employer is asking for my latest increment letter for BGV. Unfortunately, I didn't download it before leaving and no longer have portal access.

Has any former Accenture employee been able to get their increment letter after leaving? Whom did you contact, and what was the process? I have already emailed Exit Management but need it urgently.

Hi, I joined deloitte india 6 months ago as an azure data engineer for consultant position. I am unable to secure any project till now in last 6 months. I barely recieved 2 calls. Resource managers aren't responding to me. any one of you has openings for azure data engineering and Databricks in your account please refer me.

What are ur insights shd I enter as a Data analyst and go internally to data Engineering. Or try data engineering off campus directly.

Any courses or certifications that will help me severely. Something like Amazon badge will do me any good?(As a fresher)?

Has anyone given a F2F in Barclays for data engineer 5yoe? Idk what to expect bcuz i never gave a F2F interview ever before. Any tips fromm the experienced folks?

I'm looking forward into data engineering so what to do ??