How To Become a Big Data Developer

In a world where data is playing an ever-increasing role in driving business development, the demand for big data developers is on the rise. And when 80-90% of the data available isn’t structured, there’s a need for big data professionals to step in and help companies analyze that information.

One of the main reasons for this growth is the insights offered by data today. Consumers produce enormous amounts of online data about their preferences, and companies can gather information about market trends to help them make more informed business decisions. As a result, companies that analyze data will have an advantage over those that don’t. Other uses of big data analysis exist in finance, health care, manufacturing, security, and numerous additional fields.

Big data developers help companies analyze that information. They work with data scientists and IT teams to build data analysis tools and applications to provide insights into data.

But developers have their work cut out for them. With so much data generated, they need to be able to process large volumes of data, regularly consume and process new information, and find a wide variety of data sources to perform analysis on.

This guide will go into the details about how to become a big data developer. You’ll learn about the skills, education, and basic foundation you’ll need to get started with this career. You’ll also learn how to find your first big data job and grow your career.

Develop the essential skills for big data developers

As a big data developer, you’ll need many skills to handle the job. Let’s examine the primary skills developers need to work in the field.

• Programming languages. Big data developers should know programming languages for working with data. They will use languages like Python, Java, and Scala to read data from databases and manipulate it for use in projects and build reports.
• Structured query language (SQL). Developers must understand SQL to query data for use. SQL is a query language that allows developers to call databases and access and retrieve data based on parameters.
• Big data technology. Big data technologies give developers more tools to retrieve and manipulate data. For instance, Hadoop allows developers to build distributed systems for processing data, while Apache Pig analyzes large datasets with programming and optimization tools. Other tools in this ecosystem are Hive, HBase, Sqoop, Flume, HDFS, and YARN.
• NoSQL. NoSQL databases like MongoDB and Cassandra help data analysts analyze unstructured data. Instead of using relational databases (as with standard SQL), NoSQL databases have no predefined database schema and can offer scalability and agility for developers who need it.
• ETL (extract, transform, load). Big data developers should understand data processing principles such as ETL. These techniques will help them transform raw data into usable formats for data analysis.
• Problem-solving skills. Strong problem-solving skills are vital to big data professionals. They will deal with complex data structures and need to develop solutions to turn those structures into valuable insights for business owners and managers.
• Data analytics and data science. Developers should have an understanding of data analytics and data science. They will use concepts from data science—such as algorithms, databases, programming languages, hardware infrastructure, and others—to design large data processing systems that extract and transform data. Data analytics will help developers take that information for analysis.
• Machine learning. Understanding machine learning (ML) can help big data developers take advantage of new artificial intelligence (AI) tools. Predictive models assist developers in using data to forecast future performance and pick the course of action with the best chance of a positive outcome.
• Data processing tools. Big data developers should understand data processing tools like Kafka. These tools help developers create real-time processing environments for data analysis.

Meet educational background requirements

Having the proper education will be vital for becoming a big data engineer. It’s a more specialized developer role—so you’ll need more than general programming knowledge to get started.

Big data developers should understand data structures, algorithms, data science, data visualization, and other technical topics. Here are some educational paths you can take to get there.

• Bachelor’s degree. A bachelor’s degree in a related field like computer science, data science, or mathematics will give you the foundational knowledge required for big data development. It will introduce you to algorithms, data structures, and the rest of the required foundational programming knowledge. It will also give you theoretical knowledge in computer science—which will help when learning other technical concepts.
• Master’s degree. A master’s degree will give you a more specialized education in big data. Unlike the bachelor’s degree—where the education is more generalized—you’ll specialize in a topic for this degree. Use your time getting this degree to learn about specialized topics like distributed systems and machine learning.
• Certification. Big data developers can also get certified when learning about big data roles and programming topics. For instance, IBM offers the Data Science Professional Certificate for experienced developers, while the Associate Certified Analytics Professional offers certification for people without practical data analytics experience.

Build a strong foundation

You’ll need a strong foundation to get a big data developer role. You can learn a lot on the job—but practical experience alongside your theoretical knowledge will give you an edge over other job candidates and may lead to better big data developer job opportunities.

For starters, try to tackle hands-on big data problems. For example, you can visit the GeeksforGeeks practice section to get a large number of practice problems for dealing with data structures. Once you’re ready to move to data analysis applications, you can visit Kaggle to download sample datasets and read guides.

Many online communities where you can get help are also available. You can join the open-source community and contribute to a big data project. Many developers get their start by contributing to project documentation while they build their developer skills and learn the project’s code.

Applications like Discord offer online communities you can join. If you don’t want to download an application, investigate online forums like Analytics Vidhya. You can interact with other beginners and experienced professionals while building your foundational knowledge.

If you have any gaps in knowledge you’re worried about, online courses are available to help. Platforms like Udemy and Coursera offer training for many topics related to big data.

Gain professional experience

Once you have your foundational base, gaining professional experience will be your next goal. You may not be able to start directly as a big data developer, but many data science professionals start in related roles such as software engineering.

If you’re already in a software development or programmer role, look for opportunities to transition to a data project. Identify the type of work you want to contribute to.

For example, a big data developer will contribute more to building the infrastructure for data generation, while a data analyst will build the tools and use scientific methods to analyze that data. On the other hand, a data scientist may take a big-picture look at data and perform more advanced tasks.

If you see no opportunities in your current organization, you may need to look for opportunities elsewhere. Check job postings for entry-level data science jobs or internships.

This is also the time to use your new skills to contribute more to open-source projects, such as on GitHub. Take the project Prefect, for example. It’s a tool for building data pipelines, so you can learn how to use them in practice and brainstorm ideas to improve the project.

Networking can also help you grow into a big data developer role. Look in your area for local or virtual meetups, workshops, and other professional events for data engineers. You can meet other developers, learn from them, and find potential job opportunities.

Explore specialization areas

Big data has many use cases in multiple industries. If you have a particular interest you want to pursue, figure out how to get started in the industry and look for a specific type of big data work.

Let’s look at several examples of big data in use in industry.

• Health care. Big data in health care helps with predictive big data analytics. For instance, algorithms can analyze large amounts of patient data to look for trends in patient problems. These trends are useful in predicting potential health issues in patients—which helps doctors make better recommendations. Other data-driven insights provided by big data can help track the spread of disease outbreaks, analyze medical images for disease, develop new drug therapies, and provide personalized medical recommendations to patients.
• Cybersecurity. A lot of data flows through computer networks, and the large quantity makes it hard for security specialists to analyze manually. Big data developers help by creating the infrastructure to identify potential threats and present issues to IT teams, which helps them respond faster to cyber attacks.
• Finance. Data helps analyze financial trends and make predictions. For example, data engineers can create models to analyze stock performance and predict future movements—helping traders make smart trading decisions. Another example is fraud detection, where analysts use data to identify anomalies in financial data that indicate fraud.

On top of the different use cases of big data, there are specific big data roles to consider. Some big data developers focus more on the infrastructure. They use distributed tools to help design environments suited to quickly analyzing data. Other developers may focus more on delivery by building data visualization and web services that help the end user visualize the end result.

Compare job opportunities and average salaries

Understanding the job market, salaries, and job responsibilities will help you understand your options as a big data developer and plan your future career. To start, you’ll need to know the job description and the skills required.

Here are a few examples of big data jobs you may see.

• Hadoop developer. These developers work with Hadoop to build distributed big data infrastructure. Candidates should understand HBase, Pig, and other Hadoop tools. High-level analytical and problem-solving skills are required.
• Data architect. Data architects work to build data models and design database structures. They should understand relational database systems and how to query and manipulate data. Architects should also have experience with business intelligence, such as Power BI.
• Big data analyst. The data analyst aids organizations in analyzing large amounts of information to solve problems and identify trends. The analyst should understand tools like Tableau and Plotly and be able to extract and communicate insights from the information using data mining.

The other important factor to consider is the salary. You’ll find a range of salaries for big data jobs depending on your region, industry, and experience. According to ZipRecruiter, the U.S. nationwide average salary  for this role is $116,480 or $56 per hour.

Understand how to advance your career

Once you gain initial professional experience as a big data developer, you can look for ways to advance your career. If you want to grow into a more technical role, you can look for specialized big data jobs, such as database administrators, DevOps (development and operations) engineers, data analyst, and data architect. You may want to contribute to the big picture of a project, taking on a more strategic role as a project manager or managing other developers.

Getting a master’s degree is a great way to expand your big data engineering skillset. Master’s programs offer a more in-depth look at data science and analysis. Through your coursework, you’ll research more in-depth big data technologies.

For example, say you enjoy working with IT infrastructure. You can explore the DevOps side of big data engineering and focus on building IT infrastructure on platforms for processing data like AWS and Microsoft Azure. But if you enjoy the planning and organization part of data science, you can focus your efforts on database infrastructure and the best way to host data.

Keeping up with new technology will be another key factor to stay on top of as a big data engineer. Technology can change fast—which means you must be aware of the newest big data tools (such as AI for data analysis) and understand how to use them to make your current systems better.

Of course, you don’t need to go at it alone to keep up with these changes— a large data science community is available to help. Think of joining professional associations (such as the INFORMS Data Science Community) to connect with other data science professionals. These groups will hold workshops and other events to update members on the newest changes. You’ll also meet professionals in other companies who may be able to offer career opportunities in other organizations.

Find your next big data project on Upwork

As data for and from business processes keeps increasing, the need for big data developers will continue to rise. These skilled professionals will help businesses find data, organize the results, and surface insights that help organizations make better business decisions.

To become a big data developer, you’ll need the right education to get started in the field. This means a university degree or extensive certification course from a reputable institution. Once you build the right foundational skills through education and practical work, you can begin looking for your first big data job and advance your career from there.

If you’re an experienced big data developer ready to take the next step in your career and find your next client as a freelancer, browse Upwork’s Talent Marketplace to find big data jobs.

And if you’re a manager looking for help in leveraging the ever-growing data opportunities within your organization, use Upwork to hire the best big data developers with skills to meet your specific needs.

‍

Upwork does not control, operate, or sponsor the tools or services discussed in this article, which are only provided as potential options. Each reader and company should take the time to adequately analyze and determine the tools or services that would best fit their specific needs and situation.