Atlassian is hiring Experienced candidates for Associate data engineer. The details of the job, requirements and other information given below:
ATLASSIAN IS HIRING : ASSOCIATE DATA ENGINEER
- Qualification : Bachelor’s degree (or equivalent work experience) in a STEM field
- Post-graduate degree (or equivalent work experience) in a STEM field (e.g. statistics, mathematics, physics, econometrics, or computer science)
- 1-2 years of work experience in Data Engineering or related field
- Proficiency in Python or other modern programming language development experience
- Proficiency in SQL and relational databases experience
- Location: Gurugram, Haryana, India
Don’t miss out, CLICK HERE (to apply before the link expires)
Interview Questions and Answers for Associate Data Engineer – Atlassian
1. Tell me about yourself.
Answer:
I have completed my [Bachelor’s degree in Computer Science / Engineering / Mathematics] and I enjoy solving problems using data and programming. During my studies, I learned Python and SQL, and I have used them in college projects or self-learning tasks. I’m interested in data engineering because I like working with data pipelines, building efficient systems, and learning how large companies use data to make decisions. Now, I’m looking forward to starting my career in a company like Atlassian that focuses on technology and teamwork.
2. Why do you want to work at Atlassian?
Answer:
I want to work at Atlassian because it is a global tech company known for its strong engineering culture and great products like Jira and Confluence. I’m also excited about the company’s remote work flexibility, focus on innovation, and support for career growth. I believe it’s a great place to learn, contribute to meaningful projects, and work with experienced people who are passionate about data.
3. What do you understand by data engineering?
Answer:
Data engineering is the process of collecting, organizing, and preparing data so it can be used by others like analysts, data scientists, or product teams. It involves tasks like building data pipelines, transforming data, creating reliable data models, and ensuring the data is clean and accessible. Data engineers help make sure the right data is available at the right time for better decision-making.
4. What experience do you have with Python and SQL?
Answer:
I have used Python for writing scripts, processing data, and small automation tasks during my college projects or learning courses. I know how to use common libraries like pandas and NumPy. In SQL, I have written queries to retrieve data, use joins, filter records, and do aggregations. I’ve practiced building queries on sample databases and I feel confident in using both tools for solving basic data problems.
5. What is a data pipeline? Can you explain it simply?
Answer:
A data pipeline is a series of steps that move data from one place to another, usually from a source like a website or database to a storage system like a data warehouse. Along the way, the data might be cleaned, transformed, and checked for errors. Pipelines help automate this process so that the data is always ready for use by other teams.
6. How do you ensure data quality?
Answer:
To ensure data quality, I check for missing or duplicate data, make sure data types are correct, and compare results to expected values. I also try to understand the source of the data and make sure it is reliable. If I build a pipeline, I add checks or alerts to catch problems early and make sure only clean and correct data is used.
7. Have you worked on any data-related projects?
Answer:
Yes, in one of my college projects, I worked on analyzing customer feedback data. I used Python to clean the text data, remove duplicates, and perform basic sentiment analysis. I also stored the data in a SQL database and created queries to generate reports. This project helped me understand how raw data can be turned into useful information.
8. What are your strengths as a beginner in data engineering?
Answer:
My strengths are that I’m good at problem-solving, I learn new tools and concepts quickly, and I’m comfortable working with both code and data. I also pay attention to details, which helps in checking data quality. I enjoy working in teams and I always try to ask questions and improve my skills through feedback and learning.
9. Do you know what SaaS means and how it relates to data?
Answer:
Yes, SaaS stands for Software as a Service, which means software is offered over the internet and used by many users. In SaaS companies, data is very important because it helps understand how users interact with the product. Data engineers help collect and prepare this data so teams can track product performance, user behavior, and make better business decisions.
10. How do you keep improving your technical skills?
Answer:
I regularly practice coding on websites like LeetCode or HackerRank, take online courses on platforms like Coursera or YouTube, and read blogs or watch tutorials to learn new concepts. I also try to work on small projects that help me apply what I’ve learned, and I stay updated with tools and technologies used in the data field.
11. What would you do if a data job you created failed at night and you found out the next morning?
Answer:
I would first check the logs and error messages to understand what went wrong. Then, I would fix the issue, restart the job, and make sure the data is correct. After that, I would try to find the root cause and put a fix in place, like adding a check or an alert, so the same problem doesn’t happen again.
12. Why should we hire you for this role?
Answer:
You should hire me because I have the right technical foundation, I’m eager to learn, and I’m passionate about working with data. I understand the basics of data engineering, and I’m ready to take on real-world challenges and grow with the team. I believe I can contribute to Atlassian by being a reliable team member who is always focused on learning and doing quality work.
Final Tips for Candidates:
Learn the basics of ETL (Extract, Transform, Load) processes.
Practice Python (especially pandas) and SQL regularly.
Get familiar with data visualization tools like Tableau or Power BI, even at a basic level.
Show that you are a fast learner, good communicator, and team player.
Be ready to talk about any small project you’ve done with data.
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