Data Engineer vs Data Analyst: What’s the Differences

The era of Big Data requires a high level of skill and knowledge. As companies begin to collect more data than ever before, they require the help of professionals who can manage this abundance of information. Data Engineers and Data Analysts are among the most in-demand professions in this sector. However, it is important to note, that even though these two occupations are closely related – they are different in terms of focus and the tools they use.

In this article, we will compare the key roles of data engineers and data analysts. We will look at their job roles & responsibilities, their specialties, their skills, how much they earn, and what they can expect in their careers.

Who is a Data Engineer?

Data engineers form the backbone of any data-driven company as it is their job to create the entire infrastructure that helps businesses utilize data effectively. This hand-in-hand architecture design with automated software solutions helps companies be as efficient as possible. They are responsible for building effective data pipelines and managing the volume and quality of the data. Data engineers ensure that all data is accessible, transformed, and stored appropriately.

Data engineers ensure that raw data can be transformed into usable formats for analysis and decision-making. The understanding of ETL flows results in an automation souring rate. A few of the major tasks that data engineers perform are:

• Data Engineering Construction: Designing platforms for gathering and organizing data that flows through major databases such as un-arranged data.
• Data Pipeline Development: This includes the designing of automated systems to transfer various layers of data between computers and databases.
• Database Development: The data engineer’s primary job is to prepare transactions between un-arranged, semi-arranged, and arranged databases such as Plus SQL and No SQL databases.

A data analyst is an individual who performs the data analysis through a defined process and extracts insights from it. Numerous tasks are involved in the process, including data cleaning, analysis, interpretation, and visualization through the use of tools and procedures. They share their work to enhance decisions made in business by performing trend analysis or finding correlations in given data.

Data analysts bridge the gap between raw data and actionable insights. Programming, databases (like SQL), and cloud platforms are their areas of competence, and they maintain the data ecosystem’s security and efficiency.

A few of the major tasks that data analysts perform are:

• Data Cleaning: Start and end process ensuring data quality by deletion of duplicates and missing data.
• Exploratory Data Analysis (EDA): Applying statistical processes to data and understanding trends in it.
• Visualization: Using graphics to represent data findings through maps, dashboards, and pie charts.
• Reporting: Feedback is made in a particular format with ideas and proposals for the plans.

Data Engineer vs Data Analyst: Roles and Responsibilities

Data Engineer

• Infrastructure Development: Creating clouds and frameworks for data storage and data handling on a larger scale.
• Pipeline Automation: Data transferring from on-premise systems to different locations by designing ETL pipelines.
• Collaboration: Interacting with data engineers or data scientists to prepare data for models.
• Data Governance: Providing policies and processes for the optimum quality, safety, and legality of the data.
• Tool Integration: Installing systems that allow data to be extracted, stored, and searched quicker and easier.

Data Analyst

• Trend Analysis: Understanding the trends that affect the sale or trends that change between the users.
• Business Reporting: Reports that give tips to business owners over specific time frames.
• Scenario Simulation: Predictive modeling to forecast events considering certain criteria based on prior events.
• Stakeholder Interaction: Translating technical insights into non-technical and highly relevant insights for the firm.
• Problem Solving: Having data that is directly related to the questions i.e. what might have caused a decline in sales the last quarter?

Comparing Skills and Education Required

Although analytical capabilities are necessary for both positions, their training and skill sets are very different.

Skills required for Data Engineer and Data Analyst

Educational Requirements for Data Engineer and Data Analyst

Data Engineer

• Degrees: Degree in Computer Science, Information Technology, or related disciplines are the most appropriate for becoming a data engineer.

Data Analyst

• Degrees: Such areas that lead to this degree include Statistics or Mathematics, Computer Science, Economics, or Business Administration (like BCA, B.tech, MBA in IT, or E-MBA).

Tools and Technologies Used by Data Engineers vs. Data Analysts

Data Engineer Tools and Technologies

1. Programming Languages

• Python: It is perhaps the most common language for writing scripts, creating data pipelines, and automation of processes.
• SQL: One of the most important languages in the retrieval of data from structured databases, working with relational databases and data warehouses.

2. Data Storage & Databases

• Relational Databases: MySQL, PostgreSQL, etc., are used as relational databases. These are large-scale data systems.
• NoSQL Databases: MongoDB, Cassandra, etc., are used in the storage of semi-structured and unstructured databases that are not amenable to tables.

3. Big Data Technologies

• Apache Hadoop: An open-source software for increasing the size of datasets on distributed systems.
• Apache Spark: Provides a much better performance as a data processing engine than Hadoop in most real-time data processing workloads.

4. Cloud Platforms

• AWS(Amazon Web Services): Comprising services like Amazon S3, Amazon EC2, and Amazon Redshift.

5. Data Orchestration and Workflow Automation

• Apache Airflow: A tool deployed in the automation of complex data workflows and pipeline processes.
• Luigi: A Python module for constructing huge data pipelines that resolve dependencies and schedule tasks.

Data Analyst Tools and Technologies

1. Programming Languages

• SQL: A popular language used for data manipulation in and retrieval from relational systems databases.
• Python: Data analysts can utilize Python to handle statistical analysis, data cleaning as well as some machine learning tasks.

2. Data Visualization Tools

• Tableau: A popular BI tool that enables an analyst to effectively create and share interactive visual reports and dashboards.
• Power BI: A tool from Microsoft that provides excellent integration with Microsoft ecosystems allowing users to create better visuals and dashboards.

3. Spreadsheets

• Microsoft Excel: Remains one of the key tools for data analysts in data management, data organization, and data analysis.
• Google Sheets: Provides similar benefits as Excel but operates in a cloud-based environment which allows multiple users to work on a file simultaneously.

4. Data Warehouses

• Google BigQuery: A contemporary and highly scalable cloud data warehouse that allows analysts to perform millions of complex queries on mass amounts of data within seconds.
• Amazon Redshift: Operates on the cloud, an Amazon Web Services product that enables analysts to run queries over complex structured data.

Data Engineer Vs Data Analyst: Key Differences

Salary of a Data Engineer Vs Data Analyst

United States

• Data Engineer: The national average wage for a Data Engineer in the United States is around $120,000 yearly. The average salary for starting Data Engineer jobs is $90,000, however, in his senior position, the salary increases to over $150,000 depending on the firm and the region.

• Data Analyst: The roughly salary of a Data Analyst in the US is about $75,000 yearly. Starting positions earned an average base salary of around $55,000.

India

• Data Engineer: The salary of a Data Engineer in India on average is around 9 LPA. Freshers data engineers will expect their starting salary to be around 4 (four) lakhs per annum. More experienced professionals, especially those working between the 5-9 years mark can expect a salary ranging from 11-18 LPA. Senior positions can expect a salary package exceeding the 20 LPA ceiling.

• Data Analyst: The average earning of data analysts in India per annum is around 6.5 LPA or 6.5 lakhs per annum. The lowest salary that a data analyst can receive is around 4 LPA, which is true for entry-level positions. The average earnings of mid-level positions like that of analysts are expected to be around 6 to 10 LPA or more, and senior positions can finally earn up to 11 LPA.

Data Engineer Vs Data Analyst: Career Path

Career path to become a data engineer

1. Entry-Level Roles:

• Data Engineer Intern: Developing a basic understanding of data pipelines, ETL, and databases.
• Junior Data Engineer: Helping to set up and support simple data solutions and working with industry tools such as HADOOP, Spark, or AWS.

2. Mid-Level Roles:

• Data Engineer: Constructing and fine-tuning data infrastructure capable of large-scale data coordination while still ensuring the data is of high quality.
• Big Data Engineer: Aspiring to utilize big data tools and frameworks to work with and analyze massive amounts of information.

3. Senior-Level Roles:

• Senior Data Engineer: Overseeing data infrastructure projects, giving support to more junior engineers, and working on data security and governance strategies.
• Data Architect: Creating systems for the organization, maintaining networks, and developing data strategy practices.
• Solutions Architect: Designing data architecture that fits firm requirements while considering future growth and usability.

4. Advanced Specializations:

• Machine Learning Engineer: Incorporating components of data engineering during the process of training a machine learning model.
• Cloud Data Engineer: Developing target databases or deploying formerly designed models in AWS or other providers’ clouds.

Career path to become a data analyst

1. Entry-Level Roles:

• Data Analyst Intern: Applying basic data cleaning, preparing reports, and visualizing data in its basic forms.
• Junior Data Analyst: A student grappling with exploratory data analysis and learning to use tools like Excel, SQL, and Tableau.

2. Mid-Level Roles:

• Data Analyst: Working with comprehensive analysis, developing dashboards, and reporting insights that will make a change.
• Business Intelligence Analyst: Executing dashboards and reporting, usually across various departments within the company.

3. Senior-Level Roles:

• Senior Data Analyst: Working with large data sets, doing complex statistical analysis, and mentoring junior analysts.
• Data Scientist: Getting into the areas of the models that predict the future, machine learning, and statistics.
• Analytics Manager: Working with analysts, defining what the analysis should achieve and how the findings would relate to the organization’s goals.

4. Advanced Specializations:

• Data Consultant: Working with the other side and giving recommendations and taking provides to the external clients.
• AI/ML Specialist: AI-ML is a field of trends that helps to identify and make decisions with automation and through models.

Conclusion

Data Engineers and Data Analysts are both required in an organization, and both these professionals play a very important role when it comes to using data for any specific purpose. Data engineers focus more on structuring systems that can support an immense amount of data, while data analysts focus more on providing information that can be helpful in business decisions.

These two positions have very different focuses, and recognizing this difference can be significant in helping an individual find their right career but also aiding organizations in utilizing their potential for achieving success in a world dictated by data. Explore data analytics in detail with the Certification Program in Data Analytics offered by Hero Vired with Microsoft and earn a professional certification.