“Data analytics is the future, and the future is NOW! Every mouse click, keyboard button press, swipe or tap is used to shape business decisions. Everything is about data these days. Data is information, and information is power.” – Radi, data analyst

A big name in the world of data analytics – Databricks has been showcasing increased growth all through the years and there is no looking back. As one of the leading technology stalwarts, globally, Databricks reached over $1.6 billion in revenue for its fiscal year ending January 31, 2024, representing over 50% year-over-year growth, driven by rapid product innovation.

As a unified analytics technology and a cloud-based platform, Databricks can manage all types of your data requirements with complete collaboration and accuracy. It has been proving itself as a quick, scalable, robust data intelligence platform that leverages AI-based features to assist organizations in creating AI-driven content.

As you witness the soaring popularity of Databricks, you can very well realize the growing importance of Databricks engineers – technocrats who are all set to make optimum use of Databricks as a technology and its associated tech stack.

This article takes you through the different facets of a Databricks engineer and the reasons why the demand for these resources is so high.

What is a Databricks Engineer?

Databricks, Inc. is a global data, analytics and artificial intelligence company that provides a cloud-based platform to help enterprises build, scale, and govern data and AI, including generative AI and other machine learning models. – Wikipedia

Databricks engineering includes processing and transformation of huge bulk of data to be discovered through ML models. It lays down an accurate environment in which all stakeholders – engineers, data scientists, data analysts can collaborate and garner maximum results after going through ETL processes.

Developers and programmers who are well-versed in the intricacies of Databricks are Databricks engineers. They are known to the Databricks line of activities and machine learning solutions. They know how to apply the Databricks concepts on the ETL and ELT jobs. They facilitate the transformation of data from raw origin to a warehouse, including detailed analytics and reporting capabilities.

Databricks Engineer – Roles and Responsibilities

As a competent Databricks engineer, here are the different roles and responsibilities that are supposed to be performed on a regular basis:

• Detailed, functional documentation and performance tuning for different data pipelines
• Collaboration between developers, designers, scientists and analysts for secure and robust applications
• Efficient operations in any Agile Scrum framework
• Create and implement POCs for clients or prospects
• Optimum usage of Big Data for application development
• Data streaming and analytics using modern-day data analytics platforms
• End-to-end support and maintenance of all activities related to development
• Configuration, testing and build creation
• Indulging in different kinds of reviews like code review, peer and integrated testing
• Regular upgradation of technical skills through training sessions or self-learning

Databricks Engineer – Skills Required

Becoming a Databricks engineer calls for a certain set of specific technical and soft skills, to set them a class apart from the rest. Here is a glimpse at some of the skillsets:

Technical Skills

• Bachelors’ or master’s in science or engineering
• Experience in data analytics, data warehousing, data engineering and ETL jobs
• Exposure to cloud-based infrastructure
• Known to CI/CD pipelines on Databricks with usage of different tools
• Knowledge of programming with Python, SQL
• Integration of complete Databricks pipeline
• Experience with the Agile methodology of working
• Leveraging the Databricks platforms with data engineering concepts
• Data modeling, visualization, and warehousing
• Exposure to Linux and Unix administration
• Experience with Azure or AWS working and data ingestion tools

Soft Skills

• Abiding by principles such as security, quality, governance etc.
• Written and oral communication
• Working in a team environment
• Ability to keep learning continuously
• Independent way of working

Databricks Engineer – Salary Structures

According to varied sources and geographical locations, here is a snapshot of some of the approximate salary figures:

• According to Indeed, the average salary for a data engineer at Databricks in the United States is $174,558 per year, which is 37% above the national average.
• Software Engineer compensation in United States at Databricks ranges from ₹18.88M per year for L3 to ₹94.73M per year for L7.
• According to Indeed, the average salary for a Databricks software engineer in Canada is around $127,886 per year, which is 50% more than the national average. Glassdoor estimates that the average salary for a senior data engineer at Databricks in Canada is $131,984 per year.
• As of August 2024, Databricks engineers in Australia can earn up to $150,000 per year.

A Good Read: Snowflake Vs Redshift Vs Databricks – Comparing Popular Data Management Technologies 

Databricks Engineer – Common Interview Questions

Those aspiring to become Databricks engineer need to prepare for interviews and the below list can surely be of help:

1. Why should you use Azure Databricks?
2. Why is Databricks file system used for?
3. What is caching and autoscaling?
4. Is Databricks an ETL tool?
5. What are the challenges faced while using Databricks?
6. What languages are supported by Databricks?
7. Elaborate the fundamentals of Databricks
8. Is Databricks executable on cloud-based architecture?
9. Which technology is considered a competitor to Databricks?
10. Is Databricks Azure or AWS?
11. What are the salient features of Databricks?
12. Explain the basic architecture of Databricks
13. What are the workspaces that are feasible in Databricks?
14. How is a notebook created and executed in Databricks?
15. What is Databricks Runtime and Databricks File System?
16. Is Databricks a SaaS or PaaS based solution?
17. Which platform in Databricks empowers usage of data and AI?
18. Why is Databricks so popular?
19. Which cloud does Databricks use?
20. Does Databricks need coding to be done?

Summing It Up

The year passing by and yet to come shall see an upsurge in the demand of Databricks engineers and the technology itself. As the wings of the technology soar high, so shall the requirements for its associated resources.

As a Databricks service provider, we @ Ridgeant have a profound experience in leveraging the modernized and unified data lakehouse architecture of Databricks for accelerating your organizational data strategy. Our services are meant to fasten data analytics garnering enhanced output.

Our skilled Databricks taskforce assist businesses smoothen their data management, analytics and warehousing capabilities.  Our widespread clientele has been experiencing our data engineering and analytics competencies, with strict adherence to industry standards.

You can hire a Databricks engineer from Ridgeant to experience the difference and enjoy the benefits. Contact us now!