What is Data Engineer : Courses, Skills, Salary & Career

The growth of data in the past couple of years has been exponential, and businesses across various industries are recognizing the importance of harnessing this data to gain insights and make informed decisions. Data engineers play a crucial role in managing and processing this ever-expanding pool of information.

What is Data Engineer?

Data engineers are responsible for managing, optimizing, checking, and controlling data retrieval, storage, and distribution within a company.

Data engineers dig in data sets and explore new trends in the available data to extract useful information from it. To work as a data engineer, you need to have a set of technical skills, such as a deep understanding of SQL database design, knowledge of programming languages, understanding of the company's business requirements, etc. The job of data engineers also includes creating algorithms to effectively use raw data.  

It is crucial that data engineers are aware of the business goals when working with data, especially for companies dealing with large and complex databases and data sets.

Data engineers must know -

1. How to optimize data retrieval 
2. How to develop dashboards and reports for stakeholders
3. How to communicate data trends

Larger organizations often have multiple data scientists or data analysts to help understand the data, while smaller companies may rely on one data engineer to work in both roles.

Must Explore - Data Engineering Courses

Job Responsibilities of Data Engineers 

The goal of Data Engineers is to build and maintain the data structures and technology architectures necessary for large-scale processing, ingestion, and deployment of data intensive applications. They design and build the raw data repositories and, from there, collect, transform and prepare the data for analysis. Once ready, the data scientists are in charge of putting their models into production.

As mentioned, data engineers are responsible for managing and organizing data, while keeping an eye out for trends or issues that will affect business goals. 

Some of the more common job responsibilities for a data engineer include:

1. Develop, build, test, and maintain data structures and database pipeline architectures
2. Acquire datasets that align with business needs
3. Develop algorithms to convert data into actionable information
4. Engage with cross-functional teams and business leaders to understand business goals and objectives
5. Innovate new data validation methods and tools for data analysis 
6. Identify ways to improve data efficiency, quality, and reliability
7. Conduct research for industry and business questions
8. Use big data sets to address business problems
9. Implement sophisticated analytics, machine learning, and statistical methods
10. Prepare data for predictive and prescriptive models
11. Find hidden patterns using data
12. Use data to discover tasks that can be automated
13. Deliver updates to stakeholders based on analytics
14. Ensure compliance with data governance 

What Skills Should a Data Engineer Have?

To dedicate yourself to Data Engineering you need to have a practical and specialized vision of the field of data and the new needs of companies. For example, you will need to know how data is modeled and how SQL DBs work. Data engineers also program the data intake and carry out data cleaning, data validation, data quality check, and data aggregation processes. This is to ensure that the information reaches the data scientist correctly. 

Technical Skills	Tools & Technologies
Cloud Computing	JavaScript
Data Security and Privacy	Python libraries like NumPy, pandas In R: dyplr, tidyr, Seaborn, Plotly, Matplotlib in R: ggplot2
Schemes and Models	MATLAB & SAS
Data Analysis	Apache Spark, Apache Kafka, Apache Flink
Databases such as PL / SQL or SQL	Tableau
Math & Statistics - Linear or logistic regression, decision trees, random forests, empowerment, support vector machines, factorization of non-negative matrices, K-means, etc.	Apache Hadoop, Apache Spark, Apache Kafka, Apache Flink
Data mining	Machine Learning Algorithms
Distributed storage systems	Amazon Web Services (AWS)
Machine Learning and Deep Learning	Big Data, Automation and Scripting
Visual and verbal communication	MS Excel, PowerBI

How to Become a Data Engineer?

Below are the steps that you can follow to become a Data Engineer:

Fulfill the educational requirements .

To become a data engineer, you must have a bachelor's degree in -

1. Computer science
2. Software or computer engineering
3. Applied math/physics/statistics/equivalent

To gain real work experience, you should look for an internship or an entry-level position. It would help if you also considered taking up courses on data structures, algorithms, programming, database management, or coding.  

Develop Your Technical Skills

Technical skills that you must develop and nurture over time to become a data engineer are - 

1. Hadoop/Hive
2. Java / Scale
3. Spark
4. Kafka
5. SQL and NoSQL
6. python
7. Cloud platforms like AWS
8. Algorithms and data structures
9. Distributed systems
10. ElasticSearch
11. Data storage and ETL tools
12. Machine learning
13. UNIX, Linux, and Solaris

Master Programming

You must understand that data engineers are at the intersection of software engineering and data science. So before moving on to data engineering, you must go through software engineering.

The first steps then consist of gaining fundamental programming skills. The industry standard primarily revolves around two technologies: Python and Scala.

Learn about Automation and Scripting

Data engineers must know how to automate tasks, as many of the functions you need to perform with your data may be tedious or need to be performed frequently.

If a task takes too long, automate it. You must learn to use programs like Apache Airflow to develop and scripting capabilities and program your data engineering workflows.

Understand your Databases

To be a data engineer, you must understand SQL. This is the established language, and it will not go away any time soon.

SQL is a beautiful, declarative language. It has several dialects, but you don't need to know all of them as a data engineer. What is certain is that you must be familiar with PostgreSQL and MySQL.

On the other hand, you must also learn to model data in transactional databases (OLTP) and analytical databases (OLAP). And finally, you'll need to understand how unstructured data was dealt with in databases like MongoDB.

Master Data Processing Techniques

Once you've studied the fundamentals of data processing, the most challenging training comes from there. At this point, it's time to

1. Learn to process big data in batches (Apache Spark).
2. Learn to process big data in streams (Apache Kafka or Apache Flink).
3. Load the result into a destination database (MPP Databases).

The latter are databases that use parallel processing to perform analytical queries, and you must know them perfectly.

Schedule your workflows

Finally, the last step is to schedule your render job regularly. You can keep it simple and use CRON or Apache Airflow, a tool for programming data engineering workflows.

Data Engineer Career – Job Outlook 

As per DICE’s 2020 Tech Job Report, Data Engineer remained the fastest-growing job, witnessing a growth of 50% YoY. The increasing volumes of data across industries have paved the way for more and more career opportunities in this field. Some of the popular job roles in this field are - 

• Junior Data Engineer
• Mid-Level Data Engineer
• Data Architect
• Data Science Engineer
• Senior Data Engineer
• Data Engineering Manager 
• Chief Data Officer