Who is a Data Engineer?
A data engineer is known to be a data professional whose job is to use their expertise in data engineering and programming and then build systems that can not only collect managing and converting the raw data into usable information. All this information is then used by business analysts.
What data engineers do is implement different methods for improving the quality and reliability of the data. They first take raw data from different sources and then combine all this information to create consistent and machine-readable formats. Similarly, they have to develop and test architectures that then allow data extraction and transformation for both predictive and prescriptive modeling.
Key Responsibilities of Data Engineer:
As a data engineer, you might have a lot on your plate meaning, there would be many key responsibilities on your shoulders and that is why we thought to list them down for you;
- Collect Data: A data engineer has to obtain data from the right sources. And this must be done before initiating any work on the database. Once the data is obtained and we are done with formulating a set of dataset processes, the data engineer then stores that optimized data.
- Maintain Data Architectures: A data engineer has to use a systematic approach for planning, creating, and maintaining data architectures. At the same time, they also have to keep the data aligned with business requirements.
- Extracting Historical Insights: As a data engineer, you are also required to use a descriptive data model for data aggregation and then extract historical insights from it.
- Makes Predictive Models: Another task or key responsibility of a data engineer is to make predictive models where different forecasting techniques are applied to learn about the future through actionable insights.
- Identify Hidden Patterns: A data engineer has to spend a lot of time identifying hidden patterns from stored data.
- Build Analytics Tools: Another responsibility of a data engineer is to build analytics tools that use the data pipeline for providing actionable insights into customer acquisition. With that, it also leads to operational efficiency.
- Work with Stakeholders: Data engineers have to work with stakeholders closely and assist the data and design team with all the data-related technical issues. Similarly, they also have to take care of the data infrastructure needs.
- Conduct Proper Research: Data engineers also have to conduct efficient research in the industry for addressing any issues that can be encountered while tackling a business problem.
- Automate Tasks: They also dive into data and pinpoint a variety of tasks that can eliminate manual participation.
Dive into the essential skills needed to Learn AWS Data Engineering Skills with our curated resources and expert tips.
Required Qualification/Education:
If you are interested in becoming a data engineer, then you’d need a bachelor’s degree in either computer science. A degree in other fields like;
- Applied Math
- Physics
- Statistics
Or software engineering can also work well.
With that, you would also need real-world experience that includes an internship. This can help you land an entry-level position.
Recommended Courses:
Google Data Engineer Professional Certificate
- Google Cloud via Coursera
- 4 months (4 hours weekly) of effort required
- 65,874+ students enrolled
- ★★★★★ (6,157 Ratings)
Azure Data Engineer Technologies for Beginners
- Eshant Garg via Udemy
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IBM Data Engineering Foundations Specialization
- IBM via Coursera
- 5 Months (3 hours weekly) of effort required
- 6,895+ already enrolled!
- ★★★★★ (580 ratings)
What makes you qualified for this job?
A bachelor’s or master’s degree alone can never guarantee you a great job or career. Instead whatever the skills you possess will help you stand tall in the crowd and qualify for a job as a data engineer. Below we have listed down some of the very important general and technical skills you can consider.
- You need to have a good know-how of different databases.
- Must know how to do root cause analysis on both internal and external data.
- Strong project management and organizational skills are a must.
- Must know how to build and optimize “big data” data pipelines, architectures, and datasets.
- Next, you need good know-how of message queuing, stream processing, and high scalable “big data” data stores.
- After that, you must possess strong analytics skills to work with unstructured datasets.
- You should know how to extract value from large disconnected datasets.
Knowhow of below tools/software is also a must:
- You should know how to use Hadoop, Kafka, Spark, etc.
- You must have a know-how of object-oriented/object function scripting languages which are Python, Java, Scala, and C++.
- Similarly, you should know what AWS cloud services are including EC2, Redshift, EMR, and RDS.
- Understanding of data pipeline and workflow management tools including Azkaban, Airflow, and Luigi is required.
- Lastly, you must know the stream-processing systems like Storm and Spark-Streaming.
General Desired Skills:
- A very strong level of mathematical ability is required.
- A very good understanding of the ethics of gathering and working data is a must.
- Similarly, the ability to not only analyze but model and interpret data is very important.
- You need to possess written and verbal communication skills.
- Similarly, problem-solving skills are also required.
- Accuracy and attention to detail add value.
- Interpersonal and team working skills are also valued.
Data Analysis and Fundamental Statistics
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- Coventry University via FutureLearn
- 475+ already enrolled!
- 4 weeks (4 hours / week) of effort required!
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In-demand Certifications:
Want to know some of the very best data analyst certifications you can take? Below are the names that can be of good help;
- Data Science Council of America (DASCA) Associate Big Data Engineer
- Data Science Council of America (DASCA) Senior Big Data Engineer
- Google Professional Data Engineer
- SAS Certified Big Data Professional
- Open Certified Data Scientist (CDS)
- Microsoft Certified: Azure Data Scientist Associate
Top Companies/Organizations Hiring Data Engineer
Data engineer jobs are in high demand as it is a great career choice. Even in terms of job security and excellent earning potential, data engineering is an excellent choice.
Companies which are most likely to hire data engineers at high pay scale along with other perks and benefits are AirBnB, Facebook, Amazon, Cisco Systems, Capital One, Google, and Salesforce etc.
Data engineers can work in sectors like marketing, Commerce, IT Services and sales etc.
Data Engineer Salary Statistics
This section contains salary details of the data engineer working in different major countries. Before we start, one should know that the salary figure can never be the same for any country as different countries have different criteria, requirements, scopes, and salary ranges to offer.
| Country | Average Salary (Yearly) |
| United States | $118,078 |
| Canada | CA$104,750 |
| United Kingdom | £61,221 |
| India | ₹9,96,000 |
| Australia | A$119,000 |
United States:
If you are working in the US as a data engineer, then you can expect to make an average annual salary of $118,078.
Canada:
In Canada, working as a data engineer can help you make around CA$104,750 per annual.
UK:
In England, data engineers are easily making £61,221 per year.
India:
According to the demand for a data engineer in India, you can expect to make around ₹9,96,000.
Australia:
Working as a data engineer in Australia can help you make around A$119,000 a year.
References/Sources:
- https://www.glassdoor.com
- Please note that mentioned salary stats are as of Nov, 2023.
