How to Become a Big Data Engineer
by Danielle Antosz | Published on February 04, 2025
Ever wonder who makes use of all that data that your phone, social media apps, and voice assistant gather about you? Meet the big data engineer. (But don't worry—this job is a lot less creepy than it sounds!)
These professionals are responsible for designing and building systems that turn massive amounts of data into insights businesses can use to make smarter decisions. If you've ever tried to wrangle a spreadsheet with thousands of rows, imagine doing that on a such a large scale, it could crash your computer in seconds.
That's where big data engineers come in. They create the tools and infrastructure that allow organizations to store, process, and analyze data on an epic scale, making sense of everything from customer preferences to product performance. As organizations shift to data-driven strategies, the role of a big data engineer has become more crucial than ever.
What is a Big Data Engineer?
Big data engineers manage and process large-scale datasets to help businesses effectively store, analyze, and extract valuable insights from vast amounts of data. Their primary responsibilities include building processing systems, developing data pipelines, and maintaining big data infrastructures like Hadoop, Spark, and NoSQL databases. They also collaborate closely with data scientists and analysts to address business needs.
Big data engineers have several critical duties, including:
Building and maintaining scalable data processing systems
Creating pipelines to gather and transform data into usable formats
Ensuring that data systems are secure, reliable, and optimized for performance
Collaborating with other teams to understand the business's data needs.
Managing and maintaining big data infrastructures, such as Hadoop, Spark, or NoSQL databases
Day-to-Day Activities for a Big Data Engineer
On a typical day, a big data engineer works to build and maintain big data systems. Their work often involves continuous collaboration with other data professionals to enhance the accessibility and usability of data for analysis and decision-making.
If you were to follow a big data engineer around all day, their day might look something like this:
8:00 AM – 9:00 AM: Morning Check-In and Review
Review system performance and monitor any issues or alerts from overnight
Check emails and messages for any urgent updates or collaboration requests
9:00 AM – 11:00 AM: Developing Data Pipelines
Write and optimize code for new or existing data pipelines
Collaborate with the data scientist team to understand data requirements
Test and troubleshoot pipelines to ensure smooth data flow
11:00 AM – 12:00 PM: Team Meeting
Join a daily stand-up with data engineers, data scientists, and analysts
Discuss progress, roadblocks, and upcoming tasks or changes to infrastructure
12:00 PM – 1:00 PM: Lunch Break
1:00 PM – 2:30 PM: System Maintenance and Optimization
Monitor and maintain infrastructure (Hadoop, Spark, etc.)
Identify areas for optimization and apply patches or updates to ensure security and reliability
2:30 PM – 4:00 PM: Collaboration and Troubleshooting
Work closely with the data science team to refine data models and pipelines
Resolve issues in data systems, debug errors, and improve system reliability
4:00 PM – 5:30 PM: Documentation and Reporting
Document code, system configurations, and process workflows
Prepare progress reports or data insights for stakeholders
Review any outstanding tasks and prepare for the next day
Of course, plenty of things can impact a big data engineer's day. It might be something simple, like a new product launch that requires specific insights for marketing, or a complete data failure that leaves several teams without the data they need. One thing is for sure — you're unlikely to be bored for long.
Big Data Engineer: Technical Skills
Big data engineers must be proficient in several programming languages to handle large datasets and optimize data processing systems.
Key languages you'll need to know include:
Java: Commonly used for building scalable and high-performance data systems
Python: Known for its simplicity and strong ecosystem of data manipulation libraries
Scala: Often used in conjunction with Apache Spark for big data processing due to its functional programming capabilities.
In addition to programming languages, you'll need expertise in core big data technologies, such as Hadoop, a framework for distributed storage and processing of large datasets, and Apache Spark, which provides fast, in-memory data processing. Kafka, a distributed event streaming platform, is also critical for handling real-time data pipelines.
Knowledge of NoSQL databases like MongoDB and Cassandra is also useful, as they store and query large, unstructured datasets. You'll also need a deep understanding of data warehousing, ETL (Extract, Transform, Load) processes, and cloud platforms (AWS, Google Cloud, or Azure), which is crucial for building and maintaining data systems.
Big Data Industry Demand and Job Outlook
With the explosion of data in every industry, the demand for big data engineers continues to rise. Organizations are looking for professionals who can help manage and analyze massive datasets, and this role is critical as data-driven decision-making becomes more important.
While the Bureau of Labor and Statistics doesn't have specific stats on big data engineers, they do predict a drastic increase in job growth for data scientists as a whole, with an expected 35% increase between 2023 and 2033.
Education and Training for Big Data Engineers
Most big data engineers hold a bachelor’s degree in computer science, data engineering, information technology, or a related field. This foundational education equips them with the core skills needed for data processing, system design, and software development.
While a bachelor’s degree is the standard entry point, a Master’s degree in data science, big data, or related fields can improve your job prospects and open doors to more advanced positions, especially for those looking to specialize or move into leadership roles. However, a Master’s is not strictly required, and plenty of big data engineers build their careers through hands-on experience and certifications.
Certifications that can validate your skills as a big data engineer and improve job prospects include:
Big Data Engineer Career Path and Progression
While the exact path can vary, the career progression of big data engineer is likely to look like this:
Entry-Level Positions: Many Big data engineers begin as data analysts, junior data engineers, or software engineers. In these roles, you'll gain foundational knowledge in databases, coding, and data pipelines before specializing in big data technologies.
Advancement: With experience, you might move into senior positions such as senior big data engineer, data architect, or big data solutions architect. You might also transition into related fields like data science or machine learning engineering, where a strong understanding of big data systems is highly valuable.
Alternative Career Paths: Depending on their interests and additional skill sets, big data engineers may also transition into roles in similar areas, such as data scientist, data analyst, or machine learning engineer. Some might move into specializations like data security or cloud architecture.
Big Data Engineer vs. Data Scientist
While both roles work with large datasets, their focus and responsibilities are pretty different. Big data engineers are responsible for building and maintaining the infrastructure needed to store, process, and manage vast amounts of data. Their skills often include programming in languages like Java, Python, and Scala and experience with big data technologies such as Hadoop, Spark, and Kafka.
Data scientists, on the other hand, focus on analyzing the data that big data engineers find. They apply statistical models, machine learning, and data visualization techniques to uncover insights that can be used to make business decisions. Their skills include proficiency in Python, R, and tools like TensorFlow or SQL, and they are experts in predictive analytics and building machine learning models.
Think of a big data engineer as the city planner who designs and builds the roads that data travels on, while the data scientist is a driver who uses those roads to explore and find cool destinations (insights) along the way.
How Much Does a Big Data Engineer Make?
As with most IT jobs, the salary you'll earn as a big data engineer will depend on your experience, location, and industry. Here’s a general overview of salaries:
Experience Level | Average Salary |
Entry-Level (0-2 years) | $80,000 - $100,000 |
Mid-Level (3-5 years) | $100,000 - $130,000 |
Senior-Level (5+ years) | $130,000 - $160,000+ |
Source: Glassdoor, as of December 2024
Factors that often impact salary range include geographic location, industry (e.g., finance, healthcare, tech), and the size of the company. Tech hubs (think Silicon Valley) and larger companies often offer higher salaries due to the high demand for skilled professionals.
Want to Become a Big Data Engineer?
Becoming a big data engineer requires a combination of education, technical skills, and innate curiosity. While it may take time to master the tools and technologies necessary for the role, consistent effort and the right resources will help you find a rewarding career in big data.
If you're ready to dive into the world of big data, start by gaining foundational knowledge through online courses or relevant certifications. With dedication and the right expertise, you'll be well on your way to a successful career in the fast-growing industry of big data.
Here are a few courses to get you started in big data:
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