Uber Data Engineer Interview Questions and Answers

Oops dsa sql network

Q1. Dsa based python question
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Q2. Dsa based python question on tress
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Q1. Sql question queries
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Q2. Sql question query
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Q1. Apache Spark and it's working
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Q2. Past Experience
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Q1. Git version control question
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Q2. Python med DSA question
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Q1. Walk me through your resume
Ans. 
I have a strong background in data engineering with experience in various industries.
• Bachelor's degree in Computer Science with a focus on data engineering

• Worked as a Data Engineer at XYZ Company, where I developed and maintained data pipelines

• Implemented data quality checks and automated data validation processes

• Collaborated with cross-functional teams to design and implement scalable data solutions

• Experience with clo...read more
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Q2. Tell me why Facebook?
Ans. 
Facebook is a leading social media platform with vast user base and cutting-edge technology.
• Facebook has over 2.8 billion monthly active users, providing a massive data source for analysis and engineering.

• The company has a strong focus on innovation and constantly develops new technologies and tools.

• Facebook's data infrastructure is highly advanced, allowing for complex data processing and analysis.

• Working at Facebook ...read more
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Q3. Do you know what is Spark?
Ans. 
Spark is a distributed computing framework used for big data processing.
• Spark is an open-source project under Apache Software Foundation.

• It can process data in real-time and batch mode.

• Spark provides APIs for programming in Java, Scala, Python, and R.

• It can be used for various big data processing tasks like machine learning, graph processing, and SQL queries.

• Spark uses in-memory processing for faster data processing.
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Coding questions on SQL - given 2 tables, join and find the results after the join

Majorly the interviews revolve around the following skills :
1. Advanced SQL
2. PYTHON (DSA)

Majorly the interviews revolve around the following skills :
1. Advanced SQL
2. python (DSA)

Q1. Which techniques you would have used on a system you have worked in the past to be able to optimise it further. (Deep knowledge of the databases is required)
Ans. 
I would have used indexing, query optimization, and data partitioning to optimize the system.
• Implement indexing on frequently queried columns to improve search performance.

• Optimize queries by using proper joins, filters, and aggregations.

• Partition large tables to distribute data across multiple storage devices for faster access.

• Use materialized views to precompute and store aggregated data for quicker retrieval.
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Q2. Situational questions on the ETL tools used and advantages and disadvantages of both
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Q1. Describe a complex situation you were in at workplace and how did you handle it?
Ans. 
Handled a complex data migration project by breaking it down into smaller tasks and collaborating with team members.
• Identified key stakeholders and their requirements

• Developed a detailed project plan with timelines and milestones

• Assigned specific tasks to team members based on their strengths and expertise

• Regularly communicated progress updates and addressed any issues promptly
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Q1. Medium level DSA
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Q2. 2 Medium SQL questions
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Q1. Spark programming & Big Data concepts
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Q1. Data Pipeline design
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Q1. DE concepts, Previous Experience
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Q2. Cultural and Team fitment
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Q1. Explain data architecture of your project
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Q2. Explain data lake
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Q1. Introduce yourself
Ans. 
I am a data engineer with 5 years of experience in designing and implementing data pipelines for analytics.
• Experienced in ETL processes and data modeling

• Proficient in programming languages like Python and SQL

• Familiar with big data technologies such as Hadoop and Spark
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Q2. Talk about previous project
Ans. 
Developed a real-time data processing system for analyzing customer behavior
• Used Apache Kafka for real-time data streaming

• Implemented data pipelines using Apache Spark for processing large volumes of data

• Designed and optimized data models for efficient querying

• Integrated machine learning models for predictive analytics
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Q1. Work experince , resume walk thorugh
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Q2. Location preferences,salary etc.
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Q1. SQL,python coding round
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Q2. Data warehousing concepts
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