Data Engineer, Atlassian - Naukri Code 360

Q: What do data engineers do?

Data engineers design systems that collect, handle, and convert raw data into usable information for data scientists and business analysts to comprehend in various scenarios.

Introduction🌼

So you want to be a Data Engineer at Atlassian. Now, don’t you worry you have come to the right place. In this article, we will discuss the profile of a Data Engineer at Atlassian. Before diving deep into that let’s know a little about the company first.

Atlassian Corporation is a software firm established in Australia that creates tools for software developers, project managers, and other software development teams. The corporation is based in the United Kingdom, although its global offices are in Sydney, Australia, and its US headquarters are in San Francisco.

About the job📽️

Building systems that gather, process, and store data is, in essence, the work of a data engineer. For instance, a data engineer might create a data pipeline that gathers unclean, unreliable data from many sources, such as CRM and sales data, transforms it into clean, reliable information, and then eventually distributes that information to end users, such as data scientists and analysts.

For organizations, the data engineer is essential. In order to find insights that create business value, such as sales growth, operational efficiency, and other important business KPIs, analysts and data scientists rely on a data infrastructure that they build and maintain.

Salary and Perks💸

For employees with less than three years of experience, the average Atlassian Data Engineer salary in India is ₹22.3 Lakhs per year. Atlassian's Data Engineer compensation ranges from ₹15 to ₹35 lakhs per year. Salary estimates are based on the salaries submitted by Atlassian employees.

Experience in years	Annual Salary Range
1 - 2 yrs exp	~ ₹ 15 Lakhs
2 - 3 yrs exp	~ ₹ 21 Lakhs
3 - 4 yrs exp	~ ₹ 25.5 Lakhs
> 5 yrs exp	~ ₹ 35 Lakhs

** Above salary figures are subject to change

It's always a good idea to include the perks of the job the company will provide, such as

Medical Insurance
Circle In, A parent and caregiver portal
Top-up insurance.
Provident Fund
Corporate National Pension System (NPS)
Paid time off & leave
Anniversary awards
Paid time off & leave
Employee gratuity payments
Phone & internet reimbursement

Skills, Qualifications, and Experience required🤹

The Skills, Qualifications, and Experience required by a Data Engineer at Atlassian are the following:

Bachelor’s / Masters or Doctorate in Related Field like B.E/B.Tech/MCA/M.E/M.Tech.
Strong programming skills in Python and Java (Java is good to have).
Experience building data models for effective storage and retrieval to fulfill important product and business objectives.
Experience developing scalable data pipelines with PySpark, SQL leveraging the Airflow scheduler/executor framework, or equivalent scheduling tools.
Experience working in a technical setting with cutting-edge technologies such as AWS data services (Redshift, Athena, EMR) or equivalent Apache projects.
Understanding of Data Engineering tools/frameworks and standards to improve the productivity and quality of output for Data Engineers across the organization.
You are well-versed in modern software development methodologies (Agile, TDD, CICD) and how they can be applied to data engineering.
Improve data quality by utilizing and upgrading internal tools/frameworks for automatically detecting DQ issues.
Knowledge of relational databases and query writing (SQL).
Experience with machine learning engineering.
Any of your prior deployments used a Kappa architecture.
Order Management, Entitlement, Billing, Financial, and People systems domain knowledge

Responsibilities as Data Engineer⛑️

Data engineers design and operate data systems. They create datasets that are simple to evaluate and meet the needs of the firm. Data engineers put strategies in place to improve data reliability and quality. They mix raw data from several sources to produce consistent and machine-readable representations. They also design and test data extraction and transformation frameworks for predictive or prescriptive modeling.

As a Data Engineer aspirant, you must be aware of the role of the job. Just to be on the same page and to clear any doubt or misunderstanding. Here are some responsibilities of a data engineer:

The following are some of the primary duties of an Embedded Software Engineer:

Architectural Design: Designing the architecture of a data platform is at the heart of data engineering.
Development of data-related instruments/instances: A data engineer is primarily a developer. These specialists employ programming skills to create, customize, and manage integration tools, databases, warehouses, and analytical systems.
Data pipeline maintenance/testing: During the development process, data engineers would test the dependability and performance of each system component. Alternatively, they can work with the testing team.
ML Algorithm Deployment: Data scientists create machine learning models and deploy them. Data engineers are in charge of deploying those into production environments. This requires feeding the model data from a warehouse or directly from sources, specifying data properties, managing computing resources, establishing monitoring tools, and so on.
Meta-data and Managing Data: Data can be kept in a warehouse in either a structured or unstructured format. Metadata may be stored in additional storage (exploratory data about data). A data engineer is in charge of organizing and arranging stored data using database management systems.
Monitor pipeline stability: Monitoring the overall performance and stability of the system is critical as long as the warehouse needs to be cleaned regularly. Because data/models/requirements can change, the automated components of a pipeline should also be monitored and adjusted.

Roadmap and Career map for the job🛣️

Now, we are getting close to the article's end. This section will discuss what subjects you need to master to become a data engineer. And what will be your career as a data engineer

Computer Programming: Data engineering requires a good understanding and experience with a computer programming language. Python is quickly becoming the most popular programming language, it is your best choice.
Advanced Mathematics: A data engineer should be proficient in vector calculus, differential equations, and linear algebra, as defined by advanced mathematics.
Probability and statistics: When working with large datasets, it is critical to consider numerous statistical characteristics such as mean, mode, median, and so on, as they efficiently summarize and classify the data.
Database Management Systems: Database management solutions, which aid in the administration of massive datasets, is a part of data engineers' daily lives. These programs make it simple to edit and query databases.
Cloud Services Providers Platforms: Engineers that can work on cloud computing tools are in high demand as firms increasingly prefer to invest in cloud computing for data storage rather than bulky hardware systems.
Big Data Engineering Tools: A data engineer frequently works with vast amounts of data. To accomplish so, a data engineer will most likely be asked to learn big data tools like Apache Spark, Apache Hadoop, and Apache Hive.

The data engineer career path is rewarding since it allows you to develop and construct data applications. It's a very competitive industry to enter because it's one of the most in-demand jobs in tech right now. Candidates must have more than simply technical skills. They must have a solid understanding of how data and pipelines contribute to business value.

Junior Data Engineer: The work of a junior engineer is typically to manage data infrastructure rather than construct and scale a whole pipeline. A junior data engineer works on projects such as debugging, object-oriented programming, and introducing minor features while reporting to a senior data engineer.
Mid-Level Data Engineers: At this stage, mid-level data engineers begin to take on more aggressive project management responsibilities. The mid-level data engineer begins to work more closely with multiple departments, designing and building business- and product-oriented solutions with product managers and data scientists.
Senior Data Engineers: Senior engineers are more hands-on in the development and maintenance of data collection systems and pipelines. This role often necessitates considerably greater cross-functional collaboration with the data science and analytics teams in order to design pipelines optimized for deeper learning and analysis.

Preparation Tips and Resources💡

To prepare, we must have a collection of good resources. As for Data Engineer at Atlassian, refer to these resources given below:

To prepare for these interview rounds, here we have a few more resources for you

Also, prepare yourself with attractive answers to the below-mentioned questions:

The time you disagreed with the team and how you dealt with that.
Explain a situation when you were leading a group.
In a situation where your client has unreasonable demands, how do you respond to them?
Where you take a leadership role formally or informally.
The time you were at risk for any particular challenging project.

While giving your interview, just be relaxed and give answers calmly and confidently.

We wish you all the very best.🤗

Frequently Asked Questions

What is the average time it takes to become a data engineer?

You may quickly become a data engineer in as little as 6-7 months if you follow the right learning path. But again, it depends on your hard work.

How much Python do you need to know to work as a data engineer?

First, you should focus on learning different data kinds, file management, and loops. Following that, the more you work on industry projects, the more you'll learn.

Are Atlassian and Jira the same thing?

Jira Software serves as the hub of Atlassian's world-class range of developer tools, from concept to launch.

What do data engineers do?

Data engineers design systems that collect, handle and convert raw data into usable information for data scientists and business analysts to comprehend in various scenarios.