Nihilent Data Engineer Interview Questions and Answers

Q1. Data warehousing related questions
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Q2. SQL scenario based questions
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Q3. Project experience
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I have experience working on projects involving data pipeline development, ETL processes, and data warehousing.
• Developed ETL processes to extract, transform, and load data from various sources into a data warehouse

• Built data pipelines to automate the flow of data between systems and ensure data quality and consistency

• Optimized database performance and implemented data modeling best practices

• Worked on real-time data pro...read more
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Q4. Python Based questions
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Q5. AWS features and questions
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Q1. Similar to first round but in depth questions relatively
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Q2. Asked about career goals and stuff
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Q1. General work related conversation
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Q2. Salary discussion
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Q1. Python Lambda Function
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Q2. What are pods in Kubernetes
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Pods are the smallest deployable units in Kubernetes, consisting of one or more containers.
• Pods are used to run and manage containers in Kubernetes

• Each pod has its own unique IP address within the Kubernetes cluster

• Pods can contain multiple containers that share resources and are scheduled together

• Pods are ephemeral and can be easily created, destroyed, or replicated

• Pods can be managed and scaled using Kubernetes contr
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Q1. How to create DBT project
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To create a DBT project, you need to set up a project directory, create models, define sources, and run tests.
• Set up a project directory with a dbt_project.yml file

• Create models in the models directory using SQL files

• Define sources in the sources.yml file

• Run tests using dbt test command
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Q2. Explain materializations in dbt
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Materializations in dbt are pre-computed tables that store the results of dbt models for faster query performance.
• Materializations are created using the 'materialized' parameter in dbt models.

• Common types of materializations include 'view', 'table', and 'incremental'.

• Materializations help improve query performance by reducing the need to recompute data on every query.

• Materializations can be refreshed manually or automa
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Q3. What are dbt snapshots?
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dbt snapshots are a way to capture the state of your data model at a specific point in time.
• dbt snapshots are used to create point-in-time snapshots of your data model

• They allow you to track changes in your data over time

• Snapshots can be used for auditing, debugging, or creating historical reports
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Q1. Basic questions on azure databricks
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Q2. Question on spark
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Q1. SQL, data modeling
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Q1. Data pipeline and framework
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Q1. SQL questions mostly
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Q1. Azure data factory scenario based questions
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Q1. Tough sql and Azure scenario based questions
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Basic coding questions on an online platform

Case study about a use case and building ELT pipelines