My DE Journey & Experience
Two months ago,I was at my lowest of lows. 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 was thinking to give up.
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 with 12th-rejection.
Today, I'm writing this one.
Same person. Different chapter.
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. 🚀