Twitter Data Engineer Interview Guide

Interview Guide 15 Aug 2024

The role of a Twitter Data Engineer

With 2.5 quintillion bytes of data being produced every day, data engineers face the challenge of recognizing and providing data analysts with useful and reliable data. Data engineers must be able to work with complex datasets and simplify them so that they can be easily understood by people working in other fields, such as Product Managers, Data Scientists, and several more.

At Twitter, you’ll be working with business and engineering teams to deliver really time-critical results. You’ll also work very closely with folks in the Data Science and Analytics and Software Engineering departments. For insights into how data engineering roles and responsibilities are approached at leading companies, check out the Apple Data Engineer guide, which offers detailed information on the core skills and challenges in this field.

Data engineers at Twitter need to have a wide range of skills, which interviewers filter for. We’ve listed out some of the most important ones:

  • Designing and implementing data pipelines, which can then be used by data analysts and scientists for analysis. For those interested in similar roles, you might find the Google Data Engineer and Facebook Data Engineer guides useful.
  • Your interpersonal and collaboration skills, the amount of relevant experience you have in the Data Engineering field, and your ability to work in a fast-paced environment where requirements change rapidly. Skills in collaboration and fast adaptation are also valuable for roles such as the Meta DS Analytical Reasoning.
  • A really great product sense, along with being well-versed with the various technologies associated with Data Engineering.
  • An ability to measure impact and progress, so that the company knows what is working and what needs to be improved.

For additional insights into data analyst roles and related fields, you may also want to check out the Facebook Data Analyst Interview guide.

Twitter Data Engineer Interview Guide

The Twitter Data Engineering interview has 3 rounds.

The first is a technical phone screen, based primarily on advanced data structures. The second round is a Hiring Manager Interview wherein your past experiences are discussed. The next and final round is the Onsite round in which your Product Sense, Data Modeling, and SQL skills are assessed. 

Relevant Guides

Technical Phone Screen

Overview

This is a technical telephonic screening round.

What the interviewer will assess

The interviewer will majorly assess basic programming skills revolving around data structures. Your speed, efficiency, as well as approach towards the problem will be tested. It is generally 45-60 minutes long. The Walmart Data Engineer Interview Guide offers insights into practical data engineering skills and expectations.

Tips

  • Practice data structure questions, and have a solid grasp of advanced data structures. 
  • Have a good command of Java and Scala.
  • Have an in-depth understanding of SQL.
    For a comprehensive preparation approach, you can also explore the Microsoft Data Engineer, Netflix Data Engineer, and Apple Machine Learning Engineer guides to gain a broader understanding of data engineering roles and expectations.

Interview Questions

  1. Implement the Trie data structure containing insertion, deletion, and search operations.
  2. Implement a hashmap and ArrayList in Java.

Note: Basic questions based on Scala language may be asked.

Note: Instead of a Technical phone screen, Twitter may give a take-home 2-3 hour coding challenge, wherein a few coding questions (generally 2-3) are given and you have to provide a solution in the form of code. The tips and questions we’ve provided above will be valid for the take-home challenge as well.

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Interview with Hiring Manager

Overview

In this interview, you’ll be talking to the hiring manager(s). It will typically cover topics around your background, and they’ll be keen to understand how relevant your experience is. Expect them to dig deep into your past, the work you did, and Data Engineering concepts in general.

What the interviewer will assess

  • Your expertise in concepts of data engineering.
  • Whether you are a good fit for the company. For this, you might explore the Meta Data Scientist guide, which provides insights into the cultural fit and role expectations.
  • Whether you are enthusiastic and passionate about the role. You could look at Amazon Machine Learning Engineer guide for examples of how to convey passion in technical roles.

Tips

  1. Understand the culture of the company, and make sure to highlight that you understand and respect it.
  2. Convince the manager that you are passionate about the job. Check out the Meta DS Initial guide for tips on aligning with company culture and expectations.
  3. Companies really value quick learners. Don’t forget to mention such skills when speaking about your previous experiences. They’re best demonstrated through examples, so have some stories ready. The Doordash Data Scientist and Atlassian Data Scientist guides may provide useful insights on how to present your adaptability and learning capabilities.
  4. Highlight relevant experiences which accentuate your skills related to data engineering.

Interview Questions

  1. Why do you want to work at Twitter? 
  2. What are some previous projects you’ve worked on in the field of Data engineering?
  3. Questions based on experiences -
    • Describe a situation in which you had to meet tight deadlines.
    • Tell me about a time when you made a mistake. What lesson did you derive from it?

Onsite Round

Overview

The onsite round is usually considered the toughest and most technical round. The interviews will be based on -

  • Product Sense
  • Data Modeling 
  • SQL

What the interviewer will assess

  • Your command of SQL, and your clarity in the execution of queries. For practice with SQL and related database skills, the Google Data Analyst guide are valuable resources. They offer in-depth questions and scenarios to test your SQL skills.
  • Your understanding of Java and Scala. For more specific insights into how Java and Scala are utilized in data engineering roles, refer to the Amazon Data Engineer guide.
  • Product sense: How well you can think about the needs of the product, and express these needs as technical solutions. 

Tips

  1. Analyze the question properly, don’t be in a hurry to give a solution.
  2. Confidence is key. Think about the problem from various perspectives, choose the best solution, and explain it confidently.
  3. Practice various scenario-based questions.

Note: You may be asked to implement general programming questions based on Data Structures.

Interview Questions

  1. What is a window function? 
  2. Explain how the upsert command works. 
  3. What is deep copying? Explain with the help of an example. 

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