DataOps engineers are responsible for designing the data assembly line that allows data engineers and data scientists to gain insight from their analytics and research. DataOps engineers use processes and technologies to improve the speed and quality of projects being worked on. The DataOps philosophy can transform data teams, resulting in smaller development times, improved Data Quality, and more predictable production cycles.
The DataOps engineer is a management position requiring a strong background in data technology and a good understanding of the Agile and DevOps philosophies.
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The goal of the DataOps engineer is to provide the organizational structures, and the processes and tools needed to handle the steadily increasing amounts of data being handled and stored. They use automation to streamline data processing in real-time, and to increase the reliability of data analytics. DataOps emphasizes automation, and cooperation and collaboration between data engineers, data scientists, and analysts.
DataOps is based on the Agile and DevOps philosophies, and is designed for developing and delivering analytics efficiently. It supports bringing DevOps teams together with data scientists and data engineers, and offers the tools, processes, and organizational structures to support the data-focused enterprise. The DataOps philosophy is used throughout the data lifecycle, with a focus on including human creativity as part of the work and development processes.
The four Agile principles follow, with two focused on social behavior, and two focused on technical concerns:
- A project should be organized around motivated individuals. It should provide the support, as well as the environment they need, and trust them to finish the job.
- The most efficient way to communicate with a development team is in face-to-face conversations.
- A consistent focus on excellence and good design supports agility.
- Simplicity means focusing on the amount of work “not yet done,” and is essential to the philosophy.
In 2022, DataOps engineers typically averaged $92,468 in the United States. Universities and colleges are not yet providing degrees in DataOps engineering, meaning a large number of people are “promoted” to the position. If the company you work for does not currently have a DataOps engineer, and you have an interest, start promoting yourself.
The Qualifications for a DataOps Engineer
Many DataOps engineers have a background in software development, which is where they learned about the DevOps and Agile philosophies, while others were data engineers who were promoted. Most DataOps engineers have a degree in computer science, and are fluent in multiple coding languages.
DataOps engineers need to have a strong understanding of the different development approaches and they should have good people skills. As a manager, they need to adopt a big picture approach when planning projects.
The necessary technical skills include experience with:
- Python and SQL (and/or other computer languages)
- Implementation projects
- Developing and delivering products in data analytics, data pipelines, or Data Management (this is required)
- Cloud technologies, such as Google’s cloud platform, AWS, and others
- Unit testing and integration frameworks
The Problems Facing a DataOps Engineer
A DataOps engineer has control over the operations and processes of an organization, and faces a variety of problems and challenges when organizing the workplace culture and setting up a project. Some of these problems are redundant, and worth learning how to deal with in advance:
Fixing Bugs: Identifying and eliminating bugs in services and products often requires feedback from outside sources, such as customers. Good communication can greatly accelerate the bug elimination process and promote long-term business relationships.
Productivity: Optimizing productivity is the goal. Traditional development practices involve communications through several tiered structures. When using a DataOps model, however, everyone involved in the project communicates in real-time, without hesitation, which streamlines the process.
Goal Setting: Setting goals requires an understanding of how a project will progress. DataOps provides easy access to data, allowing development teams to get feedback on their own performance and the business’s performance.
Limited Collaboration: DataOps requires collaboration (and communication) between departments, which in turn promotes smooth operations. Collaboration supports teamwork and streamlines development.
Slow Response: Businesses often have trouble with managing development requests, primarily because of expectations/assumptions that were never communicated. A certain amount of clumsy communications can take place in a hierarchical communications system. DataOps changes this, as it promotes both collaboration and good communication.
The Phases of a DataOps Project
DataOps projects work with a data pipeline, showing the flow of data as the project moves through different stages. The data pipeline should be automated to provide maximum efficiency and minimal errors. Three basic features should be included in the data pipeline’s automation process:
- The Sandbox: A data sandbox, in terms of DataOps, is a scalable development platform that can be used to gather and examine data, safely. Application data should be protected from harm happening through experimentation. Additionally, individual workstations may not have the scale needed. As a form of centralized data storage, the sandbox can team collaboration.
- Staging: The staging process involves cleansing the data. Then the data is appropriately documented, and the initial models are improved and refined as it moves through successive levels of development. The model is eventually validated and approved, when it is judged trustworthy enough for production.
- Production: The final step uses the analyzed data models for the production stage. Fully refined analytic models move to the production stage for use by data consumers.
Courses and Certifications
Free classes are a good way to gain some additional knowledge on a topic — DataOps, for example. The certification that comes with the completion of some classes can also look good on your resume. Some free classes are listed below:
Current-event questions might come up during an interview, or you might want to bring up some upcoming technology or process to show you are keeping up with the industry’s evolution. Some ways to keep up with changes in the DataOps industry follow:
- CDO Trends offers an e-newsletter titled DataOps Trends
- The DataOps Blog is an e-newsletter offered by Streamets
- DataKitchen offers a series of DataOps podcasts
While many people prefer to “wing” interviews, others prefer to prepare by anticipating and rehearsing the questions that will probably be asked. If you choose to prepare for the interview, some basic inqueries are:
- Please explain your experience in DataOps
- How much do you want to be paid?
- Why do you want to work for (company name)?
- What are some projects you’ve worked on?
- What is your experience with data analytics?
- What is your experience with DataOps?
- Please explain the Agile and DevOps philosophies
365DataScience offers 17 interview questions and three “test” questions, in Data Engineer Interview Questions And Answers 2021. It’s a data engineer interview, but the questions and answers are essentially the same, though a DataOps engineer interview would probably include some management questions.
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