Big Data Analyst: Duties, Salary, and How to Become One

Big data analysts analyze the data that businesses collect and store to support strategic decision-making. If you’re interested in working with large, complex data sets, then a position as a big data analyst could be an excellent match for your goals and interests. 

Read on to learn more about big data analysts, including the various types of jobs available and the average salary for this impactful profession. Afterward, if you're interested in unlocking value in massive datasets, consider enrolling in UC San Diego's Big Data Specialization.

What is a big data analyst?

A big data analyst is a type of data analyst who works with vast and often complex data sets that require a different set of data management techniques than those used in traditional data analysis. Big data analysts work with immense data sets, known as "big data sets," and use their data analysis skills to identify insights that inform strategic planning and decision-making. As a result, they're responsible for streamlining complicated data into actionable reports that help achieve goals and solve problems.

Read more: What Is Big Data Analytics? Definition, Benefits, and More

Big data analyst tasks and responsibilities

The tasks and responsibilities of a big data analyst vary based on what company and industry you choose to work within. Some common duties you might expect to accomplish include:

• Data collection

• Translate complex data into accessible reports

• Perform analysis on large data sets

• Verify data quality

• Collaborate with other professionals to identify goals for data analysis

Big data analyst skills

Big data analysts need to have a multifaceted skill set that helps them address this role's complex and dynamic responsibilities. While the specific skills might vary based on the industry you in which you work, some typical workplace and technical skills include:

Technical

• Advanced analytical skills

• Statistical analysis

• Predictive modeling

• Scripting and software languages

• Data warehousing and database management

• Numerical skills

• Cloud computing platforms (AWS, Azure, GCP)

Workplace

• Communication skills

• Time management

• Problem-solving skills

• Teamwork and collaboration

• Attention to detail and accuracy

Big data analyst tools

Professionals use a variety of tools to address different types of analysis and data. Some of these tools and technologies include:

• Hadoop is an open-source framework that stores both structured and unstructured data sets. 

• Amazon Redshift is a scalable data warehouse that allows you to analyze all of your stored data quickly and easily.

• YARN, or “Yet Another Resource Negotiator,” refers to a cluster management technology that helps manage resources and scheduling.

• Apache Cassandra is a scalable NoSQL database that manages data sets across multiple data centers.

Big data analyst career path

Big data analysts have the opportunity to either advance within their role or move into other adjacent job titles that also handle data analytics. For example, analysts might choose to advance into management positions, such as a database manager, or move into a business intelligence analyst role.

Other career options include big data engineer, data architect, or data scientist. Your decision will depend on what interests you or what you feel passionate about pursuing within your career.

Big data analyst salary and job outlook

Big data analysts earn a median salary of about $115,000 in the United States, according to Glassdoor [1]. Additionally, the US Bureau of Labor Statistics estimates that data scientist roles—under which big data analyst jobs fall—will see a 36 percent increase through 2033, which is much faster than average [2]. Analysts are employed across various industries, such as marketing, finance, or sales.

How to become a big data analyst

Earning a bachelor’s degree is a solid first step on your career path. Computer science, statistics, engineering, or mathematics are all good choices to help you develop the strong mathematical foundation necessary for the job. While a master’s degree isn’t always necessary, some jobs prefer employees with advanced degrees. Depending on your chosen field, you might also need to develop more specific skill sets, such as a banking background for a finance role.

Once you earn your degree, you can apply to entry-level positions in the data analysis field, such as junior data analyst, data coordinator, or SQL data analyst. These roles allow you to grow and develop your analysis and data management abilities.

Read more: 4 Data Analyst Career Paths: Your Guide to Leveling Up

Big data analyst certifications

You can also develop these skills through other means, including certifications and professional credentials. The Data Science Council of America (DASCA) offers two such certifications for aspiring or current big data analysts.

• The first is the Associate Big Data Analyst (ABDA) credential, which is meant for someone with a degree in areas like business, economics, or statistics who wishes to begin a career in big data.

• The second credential is the Senior Big Data Analyst (SBDA) certification, and you can earn this one if you are already an experienced analyst who wants to transition into big data analysis. Additionally, if you are currently working in the field of big data and wish to advance your career, the SBDA certification may be a good one for you.

Build your data skills on Coursera

Big data is fast becoming big business. Build the skills you need to excel in this impactful, in-demand field with one of the following programs on Coursera:

For foundational data analysis skills, enroll in the Google Data Analytics Professional Certificate. Learn how to clean and organize data for analysis and develop key analytical skills like data cleaning and visualizations.

To learn the fundamentals of big data, try UC San Diego's Big Data Specialization. Explore foundation big data concepts, including data modeling and management, machine learning, and data integration and processing.

To prepare for a career as a data engineer, consider the IBM Data Engineering Professional Certificate. Developing working knowledge of NoSQL and Big Data using MongoDB, Cassandra, Cloudant, SparkML, and more.