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DataOps combines data science with software engineering to create a powerful mix of technical know-how, data analytics, and process optimization. With increased opportunities in automation and machine learning, a DataOps career can be highly rewarding, with exciting challenges and growth potential.
Let’s look at what it takes to enter this dynamic industry – from what a DataOps engineer does, the job benefits, and what skills it requires – so you can decide if this is the right move for your future!
DataOps (Data Operations) is a methodology that combines the agility of DevOps with the power of data analytics. Designed to improve collaboration between IT and data-related teams, it is aimed at making the business more agile by optimizing the end-to-end lifecycle of building and leveraging enterprise data assets.
As a practice, DataOps streamlines the data pipeline process, from data acquisition to data analytics, by automating and optimizing various processes. This allows an organization to analyze data in real time and respond to changing business needs rapidly.
DataOps vs. DevOps
DataOps and DevOps are two different processes, but they work together to help achieve goals more quickly and efficiently by providing increased visibility and control over software development operations.
What separates DataOps from DevOps is the focus: One looks at how fast it can move data through a given system — whether this means streaming large volumes of unstructured data in real-time or managing existing assets within an organization’s system — whereas the latter looks at how quickly changes can be deployed without sacrificing quality or stability of service levels across multiple servers/application stacks.
DataOps offers excellent opportunities to work with cutting-edge technologies and be at the forefront of data management solutions. Professionals in the field are in high demand, and that demand will only increase as more companies recognize the value of effective data management.
The key attractions of DataOps as a career choice revolve around:
- Career opportunities
- Variety of work
According to Talent.com, the average base salary for a DataOps engineer in 2023 in the United States is $130,350 per year, with an entry-level salary starting at $87,653 per year. Rates vary according to location, experience, and the target company, but it’s becoming increasingly clear that demand for experienced DataOps Engineers is increasing daily, resulting in higher industry wages.
As the demand for DataOps increases within organizations, so too do the job opportunities. These range from traditional jobs such as data engineer or analyst to new roles like big data developer and cloud architect. These positions offer great potential for career growth and development depending on your experience level in programming, cloud computing, and machine learning algorithms.
Gaining more complex technical know-how in these areas through continued research, self-learning, or formal education courses will empower you to rise higher as your DataOps career progresses.
Variety of work
DataOps engineers have the option of working in many exciting fields with interesting challenges. With the growth of artificial intelligence (AI), big data technologies are also on the rise, creating more job opportunities within DataOps aligned with projects like image recognition or natural language processing (NLP).
Networking IoT systems or securing firewalls around wireless networks also offer many job opportunities. Other associated roles focus on leveraging existing datasets for machine learning initiatives like deep learning or boosting presentational capacities by integrating increased front-end visuals using tools like R Studio.
The DataOps career path is not for the faint-hearted, as it comes with some steep challenges. Here are some of them:
- Technical proficiency: DataOps requires technical proficiency in a wide range of technologies and skill sets, such as software engineering, DevOps practices, cloud computing architecture, analytics platforms/applications, etc., making it critical for practitioners to stay up-to-date with different tools and techniques.
- Collaboration and cross-Functional communication: Given the number of teams involved (including IT operations and business intelligence/analytics teams), effective collaboration is essential for success in the DataOps space. Building relationships across teams and communicating expectations are key to getting projects off the ground efficiently and professionally.
- Adaptability and agility: Not only does the industry change quickly, but so do data trends. This means, DataOps professionals must be quick on their feet when responding to business needs or changes in technology demands. They also need to understand internal databases structures and external data sources (such as APIs).
- Continuous improvement mindset: With every project or change comes an opportunity for improvement, whether it’s through improved methodologies or tool implementations. Practitioners must have creative problem-solving abilities and an open mind toward innovation within their specific areas.
The skills and traits required to be successful in DataOps are applicable to any analytics or data-centric career. Here are the key ones you should cultivate:
An understanding of full stack technology would be ideal, but at minimum DataOps professionals should have in-depth knowledge of the data and systems they will be working with. This includes knowledge of databases, server operating systems, and scripting languages such as SQL, Python, or MATLAB.
Being a successful DataOps engineer requires strong communication skills both within a team setting and externally when dealing with clients and vendors. Good interpersonal skills are needed to collaborate effectively with colleagues across departments during problem-solving sessions or brainstorming meetings centered on efficient ways of collecting/processing/analyzing data sets relevant to the projects at hand.
Attention to detail
Attention to detail is vital because even small changes made when collecting raw data from external sources, entering information into databases, or writing scripts to generate output can affect overall outcomes significantly. It is also beneficial to be comfortable troubleshooting errors and debugging code.
Project management expertise
It takes a blend of project management skills, such as agile development principles and scrum framework methods, and technical expertise to become an effective DataOps engineer. So having some experience in planning, coordinating, and managing data-related projects would be a great asset.
Before taking the plunge into a career in DataOps, it is important to ask yourself a few key questions:
What is my level of technical proficiency?
A DataOps position requires strong knowledge of programming languages such as Python or SQL as well as emerging technologies like machine learning algorithms. If these topics sound foreign to you, then it might be wise to consider building up such skills first before pursuing a career in this field.
Am I passionate about using technology for analyzing data?
At its core, DataOps focuses on efficiently utilizing resources and technologies to effectively manage large volumes of data or datasets that need processing quickly. This requires a deep understanding of how different types of systems interact with one another and up-to-date knowledge of the latest solutions for handling data at scale. You will need the enthusiasm for tackling these challenges head-on.
How familiar am I with DevOps processes?
As part of the workflow development process in DataOps environments, you need to understand how multiple teams work together toward common objectives while coordinating processes such as source control management practices, automation pipelines, automated testing frameworks, and deployment strategies across different delivery channels. Being able to bridge gaps between developers and operations groups is crucial when building effective software products within an organization and engaging users quickly through continuous experience updates/product rollouts.
If you’re someone who loves data analysis and management, a career in DataOps might be for you. A field that focuses on efficiently processing large amounts of data for businesses and organizations, it is constantly evolving. This means there are always new tools and technologies to learn, making the work both challenging and rewarding. Plus, with the potential for high salaries and attractive career growth opportunities, a career in DataOps can be an exciting and fulfilling choice.
Mariusz Michalowski is a Community Manager at Spacelift, a flexible management platform for infrastructure-as-code. He is passionate about automation, DevOps, and open source solutions. In his free time, he enjoys car detailing, swimming and nonfiction books.