Data Engineer vs Data Analyst: 6 Critical Factors

Vishal Agrawal • Last Modified: December 29th, 2022

Data Engineer vs Data Analyst

In the modern Big Data world, data is constantly increasing exponentially and that means there’s an ever-increasing need for techs and professionals who know how to collect, organize, analyze and work with this data.

Some of the most popular careers in tech are data-focused: Data Engineer vs Data Analyst seem like similar job profiles but that is not true. These jobs profiles require a wide range of ranges of abilities, although both of them deal with data and play a vital role in refining data strategies.

In this article, we are going to discuss Data Engineer vs Data Analyst with the help of 6 critical differences.

Table of Contents

Introduction to Data Engineer

Data Analyst vs Data Engineer: Data Engineer
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A Data Engineer is an Engineer who possesses specialization in preparing data for analytics. Data Engineering is a field that involves developing platforms and frameworks for Data analysis. A Data Engineer is responsible for designing and creating the framework for Data Analysts and Scientists to work on.

Data Engineers must know about development and testing to develop a robust framework for analysts and scientists. They must have significant experience in various data formats, data operations (cleansing, modeling, etc.), and various testing techniques to handle log errors.

Introduction to Data Analyst

Data Analyst vs Data Engineer: Data Analyst
Image Source: BrainStation

The process of extracting and generating insight from a given data set is called Data Analytics, and the person who performs this kind of analysis is known as Data Analysts.

A Data Analyst extracts the information from the dataset by performing data cleansing, data modeling, and data validations. There are several tools that a Data Analyst can use to perform data analysis effectively. 

Technology, medical, business industries are heavily invested in Data Analytics as it enables them to analyze the trends, market requirements, customer behavior, thereby making careful data-driven decisions.

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Data Engineer vs Data Analyst

Now that you have a brief understanding of Data Engineer vs Data Analyst. In this section, you will read more factors that will provide you a clear understanding of Data Engineer vs Data Analyst. The following differences between Data Engineer vs Data Analyst are listed below:

1. Data Engineer vs Data Analyst: Roles & Responsibilities

Roles & responsibilities of Data Analyst 

  • A Data Analyst should have technical knowledge about the data and use SQL to retrieve the data and transform them to generate insights. 
  • A Data Analyst should be able to perform Data cleansing, Data validation, Data modeling.
  • A Data Analyst should have great experience in ETL and ELT processes.
  • They must be able to work with higher management to understand business requirements.
  • They must have excellent presentation skills to communicate with higher management and help them reach optimum solutions.

Roles & responsibilities of Data Engineers

  • A Data Engineer should develop and deliver a robust framework that Data Analysts and scientists can use to perform exploratory analytics.
  • A Data Engineer must know about various development and testing tools and processes to build robust pipelines and handle errors.
  • They must have the ability to handle raw and unstructured data along with structured and semi-structured data.
  • They must be able to provide recommendations for data quality, efficiency, and performance of the system.
  • They should be expertise in core programming concepts and algorithms.

2. Data Engineer vs Data Analyst: Skills

Skills of a Data Analyst

  • They should have strong mathematical knowledge as Data analysis requires a lot of mathematical operations.
  • They should be very well versed with analytics tools such as Excel, SQL, BI, etc.
  • They should have good communication skills, enabling them to interact with higher management and have a problem-solving attitude.
  • They should have excellent command over the analytical suite.

Skills of a Data Engineer

  • They must have a sound knowledge of programming languages like Java, Python, Scala, etc.
  • They should have a good understanding of OS, Dockers for framework development.
  • They must have good experience in handling data, i.e. ETL and ELT.
  • They should know SQL and NoSQL databases.
  • Knowledge of BigData systems like Hadoop, Spark, Hive will add advantages.
  • Knowledge about Clouds such as AWS, GCP, Azure will help them to build on-cloud applications.

3. Data Engineer vs Data Analyst: Tools

Tools that Data Analysts Commonly Use

Tools that Data Analysts commonly use.
Image Source: Educba
  • Excel: Excel is heaven for the Finance world. Excel has excellent capabilities like Pivots, V-Lookup, Mathematical and analytical functions that generate the insights in a minute. Excel is an integral part of Microsoft Office and is commonly available in every industry/organization.

    Finance professionals heavily rely on a number of its features which can analyze complex data sources. Excel is nearly a database with rows and columns and a vast list of mathematical and analytics functions. Excel is one of the versatile tools available in the market and has several plugins that can enhance its efficiency.
  • BI Tools: BI tools are another great set of Financial Data Analysts tools that allow finance professionals to extract meaningful data from the raw data sets. The popular BI tools like Microsoft Power BI, MicroStrategy, Tableau, Qlik Sense features data cleaning and data modeling capabilities that identify Financial trends, Sales forecasting, area of improvement, and many more. 
  • R and Python: R and Python are leading programming languages with exceptional features for data modeling and extraction. Professional users can use these programming languages to build out-of-the-box capabilities to perform customized and complex statistical analyses.

    With programming language, users can build algorithms that perform regression analysis, classify data clusters, and more.

Tools that Data Engineers Commonly Use 

Tools that Data Engineers commonly use
Image Source: Intellipat
  • Hadoop: Apache Hadoop is a leading open source Big Data system that allows Data Engineers to work seamlessly with a massive volume of data. Data Engineers frequently build a framework on Hadoop to provide scalability for Analysts and Scientists to work on. 
  • Apache Spark: Apache Spark is a lightning-fast in-memory computing system that computes the transformation in-memory, thereby providing speeding solutions to the problem. It works very well with batch data as well as streaming data.
  • Kubernetes: Data Engineers frequently use Kubernetes to build an automated system of cluster orchestrations, deployment, and scaling. Kubernetes is entirely cloud-based and has revolutionized the Big Data world.
  • Java: Java is the oldest and yet most popular programming language used by many Data Engineers to build frameworks, data pipelines, infrastructures, etc.

4. Data Engineer vs Data Analyst: Programming Languages

Programming Languages used by Data Analysts 

  • SQL: SQL stands for Structured Query Language, and it is not a programming language; instead, it is a language that works with databases and tables. It is widely used for Financial Data Analysts as it has a vast library of financial functions that can be used to aggregate the financial metrics, and it works well with structured data.
  • Python: Python is the most popular language used in the Finance world. It has made revolutions in the Finance world by its exceptional library of statistical and mathematical functions.
  • Java: Java is a multi-purpose object-oriented programming language and is mainly used to create Desktop applications. Java is considered one of the most secure applications and is extensively used in the Financial and banking sectors. 
  • R: R is ranked at the top programming language for statistics and data manipulation. R helps discover and maintain the relationship between nodes, mainly used for predictions and forecasting.
  • Matlab: Matlab is the acronym for Matrix Laboratory and is widely used in the Finance world. Matlab has extensive support for financial algorithms, data manipulation, data functions, plotting and is very well suited for cross-platform integration.

Programming Languages used by Data Engineers

  • Java: Java is the oldest and yet most popular programming language that many Data Engineers use to build frameworks, data pipelines, infrastructure, etc.
  • Apache Spark: Apache Spark is a lightning-fast in-memory computing system that computes the transformation in-memory, thereby providing speeding solutions to the problem. It works very well with batch data as well as streaming data.
  • REST API: Rest API is the commonly used tool for Data ingestion. Multiple tools use Rest API. Some of them are Sqoop, NiFi, ADF, Flume, etc. 
  • Cloud Infrastructure: Cloud Infrastructure has revolutionized the Data Engineering world. With the introduction of the Cloud, people are rushing towards it due to its serverless, scalable, and managed solutions. The commonly used clouds are Google Cloud Platform, AWS, and Azure.

5. Data Engineer vs Data Analyst: Educational Background

Educational Background required for Data Analysts

  • Four years of Bachelor’s degree
  • Good Understanding of Data and business knowledge.
  • Experience in any of the programming languages and SQL.
  • A certification on relevant Data Analysis will add an advantage.
  • Should possess excellent communication and interpersonal skills.

Educational Background required for Data Engineers 

  • A Four years Bachelor’s Degree in Computer stream
  • Optional, but a Master’s Degree will help to understand core concepts and terminology.
  • Three years or more working experience as a Data Analyst.
  • Good knowledge of programming languages.
  • Experience in working with Cloud Infrastructure, Visualizations, etc.
  • Should possess outstanding communication and interpersonal skills.

6. Data Engineer vs Data Analyst: Salary and Job Openings

The salary for Data Engineer vs Data Analyst varies across locations. However, on an average: 

A Data Analyst earns an annual salary of $67,377, whereas A Data Engineer earns $116,591 per annum. For Job Openings, you can refer – Naukri, Indeed, JobServe depending upon your location.

Job postings from companies like Facebook, IBM, and many more quote much higher salary packages.

The Average pay Data Engineer get by Glassdoor is given below:

Data Engineer Average Salary
Image Source: Glassdoor

The Average pay Data Analyst get by Glassdoor is given below:

Data Analyst Average Salary
Image Source: Glassdoor

Though, salary depends on many factors such as company, demand in the geographical region, competitive companies pay, years of experience, performance, etc.

Job openings for both profiles are in demand but it also depends on the industry you are trying to pitch and what type of skills you have.


In this blog post, we have discussed in detail Data Engineer vs Data Analyst in terms of the 6 critical differences in both streams. Data Engineer vs Data Analyst may sound like a similar profile but when drilling down to its core objectives, duties, and requirements they are totally different profiles serving the same industry. Both Data Engineer and Data Analyst are essential profiles required by companies.

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