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Big Data Engineer is one of the most in-demand tech jobs(opens new window) for 2024. But what is a Big Data Engineer, and why is this career so pivotal for modern businesses?
Companies in all industries rely on Big Data Engineers to build and manage infrastructure for vast datasets. The demand for these professionals has surged as data grows exponentially. By 2025, Internet users will produce and consume over 180 zettabytes(opens new window) — or 180 billion terabytes — of data annually. 78% of data chiefs(opens new window) plan to invest more in data in 2024 to take advantage of this influx of information.
Big Data Engineers help companies leverage large amounts of data to improve decision-making. They often earn lucrative salaries and have many opportunities for advancement. However, there are a few factors to consider if you’re interested in this career. Below, we’ll explore what a Big Data Engineer does, the necessary skills, and salary.
What does a Big Data Engineer do?
A Big Data Engineer develops and maintains sophisticated data management systems. This infrastructure allows companies to process and store enormous quantities of information.
Big data engineering addresses the unique challenges posed by large-scale information. These traits characterize big data(opens new window):
- Volume: Huge amounts of data that traditional hardware and tools can’t process
- Velocity: Rapid growth
- Variety: A mixture of structured and unstructured data
- Value: Significant impact on organizations and society
- Veracity: May have inaccuracies, errors, or uncertainties
A Big Data Engineer uses programming languages and frameworks to build scalable infrastructure to handle this complex data. They also collaborate with Data Scientists and other professionals to analyze information and gain insights.
Suppose a hospital wants to use big data to improve its operations. A Big Data Engineer could collect medical histories, laboratory results, financial records, and other information. They may also work with Data Analysts and Data Scientists to interpret this data and make decisions. For example, the team could examine patient records to predict future disease outbreaks and manage resources effectively.
Key responsibilities of a Big Data Engineer
The role of a Big Data Engineer includes many responsibilities, such as:
- Data sourcing: Locate relevant, high-quality data sources that organizations can analyze and use for strategic decision-making.
- Big data pipelines: Create scalable systems and frameworks to extract, process, and transport data.
- Data collection: Use data mining methods and tools to gather information from diverse sources. This data may include social media posts, transaction records, sensor readings, and web interactions.
- Storage: Organize data into secure storage solutions, such as data lakes and cloud storage platforms.
- Processing: Develop algorithms and frameworks to clean raw data and structure it into a usable format.
- Analysis: Use programming languages and statistical methods to uncover hidden connections, insights, and trends within datasets.
- Collaboration: Work in cross-disciplinary teams with Data Analysts, Project Managers, and other professionals.
- Technical support: Troubleshoot and fix any data processing and management issues.
- Research: Continuously learn new techniques for gathering valuable data and stay updated on emerging trends in the field.
Essential skills for a Big Data Engineer
You don’t need a data science degree to start a career in this fast-growing field. However, employers do require these critical Big Data Engineer skills.
Programming proficiency
Big Data Engineers use programming languages to develop data pipelines and algorithms. Popular languages include:
- Java: Write code for data processing tasks.
- Scala: Develop data processing applications in Apache Spark.
- R: Create statistical models and visualize data.
Experience with big data frameworks
Big data frameworks are platforms designed to process and analyze large amounts of information. This software provides the infrastructure and tools needed to manage big data effectively. Here are three open-source frameworks:
- Hadoop: A Java-based framework that divides big datasets into segments and distributes them across a network for parallel processing.
- Spark: A versatile framework using in-memory processing to manage and analyze big data quickly.
- Kafka: A distributed storage system that processes big data in real time.
Database management
Big Data Engineers use databases to store, organize, and manipulate large datasets. They often build relational databases with Structured Query Language (SQL). They also create NoSQL databases for unstructured or semi-structured data.
Critical analysis
Big Data Engineers must critically analyze datasets' accuracy, consistency, and reliability. They also use this skill to assess data workflows and identify areas of improvement. Additionally, critical analysis enables them to derive insights from data and solve complex problems.
How to become a Big Data Engineer
Many employers value hands-on experience and skills over a college degree. These steps can help you gain the expertise needed for a big data engineering career:
- Develop coding proficiency: Many websites have free tutorials and resources to help you learn relevant programming languages, such as Java and Scala. You can also participate in coding challenges and hackathons.
- Practice with actual data: Data.gov(opens new window) and other organizations publish free big datasets. Use these resources to practice building data pipelines and analyzing data.
- Earn certifications: Develop specialized knowledge by obtaining certifications in data engineering. Popular certificates include the AWS Certified Data Engineer - Associate(opens new window) and IBM Data Analyst Professional Certificate(opens new window).
- Become an apprentice: An apprenticeship lets you gain valuable experience under the mentorship of a seasoned data professional. You’ll work on real projects and expand your network.
- Create a portfolio: Demonstrate your technical skills by developing projects and displaying them on a personal website. For example, you could create a data visualization dashboard and an ETL (extract, transform, load) pipeline.
Big Data Engineer vs. Data Scientist: Understanding the difference
Both Big Data Engineers and Data Scientists manage and analyze complex data. But these careers have different areas of focus and responsibilities.
Big Data Engineers develop the infrastructure to gather, process, and store big data. They design data pipelines, storage solutions, and other aspects of data systems. They ensure other data professionals can access and analyze datasets easily.
Data Scientists focus on analyzing data to discover insights and patterns. They use advanced analytics and machine learning algorithms to process and interpret information. These professionals also communicate their findings to clients, business leaders, and other stakeholders.
Many projects involve both types of data professionals. Big Data Engineers lay the groundwork by developing the necessary infrastructure, and Data Scientists use this architecture to gain insights.
The future of big data engineering
The big data landscape evolves constantly as new trends and technologies emerge.
The recent popularity of Internet of Things (IoT) technology has fueled the growth of big data. By 2025, an estimated 56 billion connected IoT devices(opens new window) will generate almost 80 zettabytes. Big Data Engineers can adapt to this change by studying IoT protocols and creating systems capable of real-time processing.
Generative artificial intelligence (GenAI) also offers new opportunities for Big Data Engineers. This tool can create synthetic datasets for algorithm training and predictive modeling. This technology also speeds up data cleaning and processing.
Career path and progression for Big Data Engineers
Most professionals start as entry-level or junior Big Data Engineers. They help senior Data Engineers design and maintain data pipelines. They also handle data quality assurance and troubleshoot issues with data processing.
As you gain experience and skills, you may advance to mid- and senior-level roles, such as:
- Intermediate Big Data Engineer: Three to five years of experience
- Lead Big Data Engineer: Five to eight years of experience
These roles come with increasing levels of responsibility. For example, Intermediate Big Data Engineers optimize data workflows and develop data models. Lead Big Data Engineers manage data projects and oversee teams of junior Engineers.
Some Big Data Engineers use their technical skills to transition into specialized roles with different job titles, such as:
- Chief Data Officer
- Cloud Solutions Architect
- Data Architect
- Data Manager
- Machine Learning Engineer
- Product Manager
- Software Engineer
Big Data Engineer salary
According to Glassdoor(opens new window), Big Data Engineers earn an average base salary of $130,000 in the United States. Most base salaries range from $127,000 to $198,000. These professionals earn between $21,000 and $39,000 in additional pay annually.
Many factors can affect salary, including:
- Certifications
- Company size
- Economic trends
- Experience
- Industry
- Job responsibilities
Salaries also vary drastically by geographic location. According to ZipRecruiter(opens new window), here are the average salaries for Big Data Engineers in the five highest-paying states:
- Washington: $153,000
- Delaware: $143,000
- Virginia: $141,000
- Maryland: $139,000
- New York: $139,000
By contrast, here are the average salaries for the lowest-paying states:
- Michigan: $103,000
- Kentucky: $103,000
- West Virginia: $98,000
- Arkansas: $98,000
- Florida: $94,000
It’s essential to consider the cost of living when comparing jobs. A lower cost of living may offset a smaller salary and allow you to enjoy a higher quality of life.
The central role of Big Data Engineers in today's data-driven world
Businesses rely on Big Data Engineers to harness the power of big data. These professionals help companies leverage valuable information to drive business and technological innovation.
A free Multiverse apprenticeship can help you launch your big data engineering career without an expensive degree. Develop a strong foundation in database management and data analytics with our 12-month Advanced Analytics program. You’ll gain critical skills, network with experienced professionals, and work on real projects.
Jumpstart your career by completing our quick apprenticeship application(opens new window). Our team will follow up to help you set up an apprenticeship.
