u/Empty_Style_6188

Hello everyone,

Used Chatgpt to correct it and write it in good way

I recently got a call from a KPMG recruiter saying my resume had been shortlisted for a technical interview. The role is Associate Consultant – Infrastructure (Data Analytics – Microsoft Fabric). She also suggested I revise Microsoft Fabric, Lakehouse, Power Apps, etc.

A little about my background:

• 1 year of experience as a Data Engineer
• SQL, PySpark, Azure Databricks, Apache Airflow, ADLS Gen2
• Worked on ETL pipelines, Medallion Architecture, and healthcare data

I reviewed the JD it was ppt, and it seems to be a mix of both data engineering and analytics. It includes data ingestion, data transformation, Lakehouse, SQL, pipeline architecture, and Fabric, but it also mentions Power BI, DAX, Power Apps, and dashboarding.

My long-term goal is to build my career as a Data Engineer, not move into a BI/Data Analyst role.

For those who have worked at KPMG or other Big 4 firms:

1. In practice, how much of this role is data engineering (pipelines, Lakehouse, ETL) versus dashboards and reporting
2. One of my long-term career concerns is platform portability. If I spend the next 2–3 years primarily working with Microsoft Fabric (Lakehouse, Data Factory, Fabric notebooks, etc.), how is that experience generally viewed when applying to Databricks-focused or Spark-heavy companies? Have any of you made a similar transition?
3. Would you consider this a good opportunity for someone who wants to stay on the Data Engineering path?

I'd especially appreciate replies from people who have worked in KPMG, other Big 4 firms, or Microsoft Fabric-based Data Engineering teams.

Thanks in advance

Hey everyone,

This is my first post in this community, so apologies if I miss anything.

I have a technical interview scheduled with Avathon for a Junior Data Engineer role. The interview panel will include 3 technical interviewers along with HR.

During my discussion with HR, she mentioned that the interview will mainly focus on:

• Python scripting
• SQL
• AWS and Terraform
• Basic of Airflow, Git , Docker
• The assignment they gave me

HR specifically mentioned that most of the interview questions may come from the assignment itself, so I’m trying to understand what level of depth I should prepare for.

About the assignment:

I was given a weather data pipeline + automation use case where I had to:

• Build notebooks to extract weather data from APIs
• Process and visualize the data using Matplotlib
• Send automated email reports using Python
• Suggest a serverless production architecture using AWS Lambda or Google Cloud Functions to automate the full workflow
• Write Terraform scripts for infrastructure automation (Lambda, scheduling, permissions, etc.)

Most of my previous hands-on experience is on the Azure side, where I worked on:

• PySpark
• Airflow
• Databricks
• Azure (ADF,ADLS Gen2,Blob Storage,Key Vault)
• SQL
• ETL pipelines
• Medallion architecture

I currently have around 9 months of hands-on Data Engineering experience, mainly in a US healthcare data engineering project.

Since this role seems more AWS-focused, I wanted to ask:

• What kind of AWS cloud questions can I expect for a junior Data Engineer role?
• What level of Terraform questions are usually asked?
• In Python scripting interviews, do they mostly ask DSA-style coding questions or practical scripting/data-processing questions?
• What level of SQL questions should I prepare for (joins, window functions, CTEs, query optimization, etc.)?
• Has anyone interviewed with Avathon before? How was the technical round experience?
• If anyone currently works/worked there, could you share insights about the company culture, projects, work-life balance, and tech stack?

Any interview tips, preparation advice, or experience sharing from community would really help me a lot.

Thanks in advance