Farfetch Data Engineer Interview Questions and Answers

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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Python DSA Leetcode style questions, prepare.

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

1.Basic SQL
2. Python based question
3.Data modelling
4. Spark
5. Cloud based questions

Sql,Python,Data Modeling and Project based questions

Q1. Salary discussion and cultural fit.
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Q2. Basic cultural fit.
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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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