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Data Analyst vs Data Engineer vs Data Scientist: Skills, Responsibilities, Salary

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Published on May 22,2019
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Data Analyst vs Data Engineer vs Data Scientist

Data has always been vital to any kind of decision making. Today’s world runs completely on data and none of today’s organizations would survive without data-driven decision making and strategic plans. There are several roles in the industry today that deal with data because of its invaluable insights and trust. In this article, we will discuss the key differences and similarities between a data analyst, data engineer and data scientist.

Before we delve into the technicalities, let’s look at what will be covered in this article:

  1. Who is a Data Analyst, Data Engineer, and Data Scientist?
  2. Skill Sets
  3. Roles and Responsibilities
  4. Salary Trends

You may also go through this recording of Data Analyst vs Data Engineer vs Data Scientist where you can understand the topics in a detailed manner.

Data Analyst vs Data Engineer vs Data Scientist | Data Analytics Masters Program | Edureka

Who is a Data Analyst, Data Engineer and Data Scientist?

Data Analyst vs Data Engineer vs Data Scientist

Data AnalystData EngineerData Scientist
Data Analyst analyzes numeric data and uses it to help companies make better decisions.Data Engineer involves in preparing data. They develop, constructs, tests & maintain complete architecture.A data scientist analyzes and interpret complex data. They are data wranglers who organize (big) data.
    • Data Analyst

    Most entry-level professionals interested in getting into a data-related job start off as Data analysts. Qualifying for this role is as simple as it gets. All you need is a bachelor’s degree and good statistical knowledge. Strong technical skills would be a plus and can give you an edge over most other applicants. Other than this, companies expect you to understand data handling, modeling and reporting techniques along with a strong understanding of the business.

    • Data Engineer

    Data Engineer either acquires a master’s degree in a data-related field or gather a good amount of experience as a Data Analyst. A Data Engineer needs to have a strong technical background with the ability to create and integrate APIs. They also need to understand data pipelining and performance optimization. 

    • Data Scientist

    Data Scientist is the one who analyses and interpret complex digital data. While there are several ways to get into a data scientist’s role, the most seamless one is by acquiring enough experience and learning the various data scientist skills. These skills include advanced statistical analyses, a complete understanding of machine learning, data conditioning etc.

    For a better understanding of these professionals, let’s dive deeper and understand their required skill-sets.

    Skill-Sets

    The below table illustrates the different skill sets required for Data Analyst, Data Engineer and Data Scientist:

    Data Analyst vs Data Engineer vs Data Scientist Skill Sets

    Data AnalystData EngineerData Scientist
    Data WarehousingData Warehousing & ETLStatistical & Analytical skills
    Adobe & Google AnalyticsAdvanced programming knowledgeData Mining
    Programming knowledgeHadoop-based AnalyticsMachine Learning & Deep learning principles
    Scripting & Statistical skillsIn-depth knowledge of SQL/ databaseIn-depth programming knowledge (SAS/R/ Python coding)
    Reporting & data visualizationData architecture & pipelining
     Hadoop-based analytics
    SQL/ database knowledgeMachine learning concept knowledge
     Data optimization
    Spread-Sheet knowledgeScripting, reporting & data visualization Decision making and soft skills


    As mentioned above, a data analyst’s primary skill set revolves around data acquisition, handling, and processing. 
    A data engineer, on the other hand, requires an intermediate level understanding of programming to build thorough algorithms along with a mastery of statistics and math! And finally, a data scientist needs to be a master of both worlds. Data, stats, and math along with in-depth programming knowledge for Machine Learning and Deep Learning.

    Now that we have a complete understanding of what skill sets you need to become a data analyst, data engineer or data scientist, let’s look at what the typical roles and responsibilities of these professionals.

    Next, let us compare the different roles and responsibilities of a data analyst, data engineer and data scientist in their day to day life. 

    Roles And Responsibilities

    The roles and responsibilities of a data analyst, data engineer and data scientist are quite similar as you can see from their skill-sets. Refer the below table for more understanding:

    Data Analyst vs Data Engineer vs Data Scientist Roles

    Data AnalystData EngineerData Scientist
    Pre-processing and data gatheringDevelop, test & maintain architectures Responsible for developing Operational Models
    Emphasis on representing data via reporting and visualizationUnderstand programming and its complexity Carry out data analytics and optimization using machine learning & deep learning
    Responsible for statistical analysis & data interpretationDeploy ML & statistical models Involved in strategic planning for data analytics
    Ensures data acquisition & maintenanceBuilding pipelines for various ETL operations Integrate data & perform ad-hoc analysis
    Optimize Statistical Efficiency & QualityEnsures data accuracy and flexibilityFill in the gap between the stakeholders and customer

     

    Now data scientist and data engineers job roles are quite similar, but a data scientist is the one who has the upper hand on all the data related activities. When it comes to business-related decision making, data scientist have higher proficiency.

    After these two interesting topics, let’s now look at how much you can earn by getting into a career in data analytics, data engineering or data science.

    Data Analyst vs Data Engineer vs Data Scientist: Salary

    Data Analyst vs Data Engineer vs Data Scientist Average Salary

    Data AnalystData EngineerData Scientist
    $59000 /year$90,8390 /year$91,470 /year

    Salary Hike - Data Analyst vs Data Engineer vs Data Scientist - Edureka

     

    The typical salary of a data analyst is just under $59000 /year. A data engineer can earn up to $90,8390 /year whereas a data scientist can earn $91,470 /year.

    Looking at these figures of a data engineer and data scientist, you might not see much difference at first. But, delving deeper into the numbers, a data scientist can earn 20 to 30% more than an average data engineer. Job postings from companies like Facebook, IBM and many more quote salaries of up to $136,000 per year.

    If you wish to know more about Data scientist salary, job openings, years of experience, geography, etc., here’s a full-fledged article on Data Scientist Salary for your reference.

    If you are someone looking to get into an interesting career, now would be the right time to up-skill and take advantage of the Data Science career opportunities that come your way.

    Edureka has a specially curated Data Science Masters course which will make you proficient in tools and systems used by Data Science Professionals. It includes training on Statistics, Data Science, Python, Apache Spark & Scala, Tensorflow and Tableau. The curriculum has been determined by extensive research on 5000+ job descriptions across the globe.

    Got a question for us? Please mention it in the comments section of “Data Analyst vs Data Engineer vs Data Scientist” article and we will get back to you.

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