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Data Analyst vs. Data Scientist vs. Data Engineer

The Demand for Data Careers

Businesses are collecting tons of data from various sources, but they aren't always sure what to do with it or what insights are hidden in the data they collect. Customers are hearing from outside sources, their boards, and news that artificial intelligence and machine learning (AI/ML) is the future, and if they aren't using it, they will be left behind. 

This fear of being left behind motivates clients to try and find any type of machine learning (ML) that they can do, even if it doesn't make sense. Many companies and individuals don't understand what it means to do ML and the steps required to conduct it successfully. To truly do dashboarding, and fully leverage data for ML, your business needs to be able to handle, curate, clean, and organize your data. 

Data analysts, data scientists, and data engineers all make your data usable, to help find insights for making business decisions, or to build machine learning and predictive products. Each plays a distinct role in this process.

Data Analyst

Data analysts serve as data consumers within an organization. They analyze data and communicate results to make business decisions. They are also sometimes called business analysts.

Data Analyst Skills

Data analysts possess excellent communication skills and are well-versed in business operations, SQL, BI tools, Python, and R. They are usually responsible for extracting, transforming, and loading (ETL) data for visualization tools like Amazon QuickSight, Tableau, and PowerBI. They work closely with business stakeholders and partners to answer analytics questions. In line with these job capabilities, data analysts often benefit from an education or background in business that can provide domain knowledge.

Data Scientist

Data scientists also consume data within an organization. However, unlike data analysts, data scientists find patterns in data to answer predictive questions about the future instead of present-focused questions. They work to solve data-related questions that involve more complex mathematics than what is normally in an analyst’s dashboards. 

Data scientists usually create machine learning models that can predict:

  • When something is going to happen (e.g., needed maintenance on a motor, the shipping cost for a product going from one location to another)
  • Item classifications (e.g., using NLP or computer vision, or other techniques to group like items together or identify an item)
  • Recommendations (e.g., personalization algorithms that provide eCommerce recommendations based on purchases from similar consumers)
  • Other use cases

Data Scientist Skills

Data scientists leverage statistics, mathematics, programming, and big data to solve business problems. They are typically well-versed in SQL, Python, R, and cloud technologies. Data scientists are challenging to classify because they often come into the field via an advanced degree like a Ph.D. or have extensive work experience where mentors guide them to operate and think like a data scientist.

Data scientists understand the algorithms, develop a scientific approach, and go about answering a question and verifying that the answer makes sense. Validating and verifying answers and making sure they make sense is a huge operational difference that defies data scientists from junior data engineers and data scientists.

Data Scientist vs. Machine Learning Engineer

Many people may claim they are data scientists when a more accurate title might be ML engineer. An ML engineer understands how to make ML algorithms work but doesn't necessarily dig deep into the problem or create the problem statement they are trying to solve.

Data Engineer

Data engineers build and optimize systems to allow data scientists and analysts to do their work. Working behind the scenes, data engineers connect to data sources to pull data into your data lake. They clean the data, create the schemas, and write the more in-depth ETL code. They often set up infrastructure pieces used by data analysts and data scientists. An effective data engineer helps save a lot of time and effort for the rest of the organization.

Data Engineer Skills

Data engineers possess strong programming skills and are well-versed in SQL, Python, cloud, big data, and distributed computing. 

They often have a college degree or an educational background in computer science, math, or other physical sciences. There is currently a lack of specific data engineering programs at the undergraduate and post-graduate levels. Data engineers usually learn their skill sets on the job and are mentored by senior employees who help them define skill sets. 


Data analysts, data scientists, and data engineers work with data in three distinct ways. 

  • Data engineers work to continuously improve data pipelines, ensuring the data that the organization collects and relies upon is accurate and available. With new data sources coming all the time, data engineers will continue to play a critical role in maintaining and building your data infrastructure. They leverage different tools to ensure the data is processed correctly and that the correct data is available to anyone who needs it. 
  • Data analysts extract new data sets based on what the engineer has built, identify trends in that data, and run analyses on outliers. Data analysts use visualization tools to gain insights into this ever-increasing data set and answer questions posed by business stakeholders.
  • Data scientists build upon analysts’ findings and research to gain deeper insights. Data scientists leverage machine learning models or advanced statistical analyses to provide your organization with what might be possible for the future. 

As data moves from engineers to analysts and scientists, your business can begin to answer important questions that your board might have, like when is that machine going to fail? If a customer buys this product and is similar to another customer, what other products can we recommend for them?

Ready to start or revamp your existing data strategy? Set your project up for success by working with an AWS Premier Tier Services Partner and AWS Data and Analytics Competency holder like Mission Cloud Services. Schedule a free consultation with an AWS Certified Mission Solutions Architect and start the conversation about what data, analytics, and machine learning can do for your business.

Author Spotlight:

Ryan Ries

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