The world of data science is rapidly changing, and it has opened up new avenues and job positions in the field of data engineering. From silicon valley corporate giants to small start-ups, every data-driven organization needs data analysts and data engineers to design, build, test and manage data to drive research, business endeavors, national security and more. Even though there are a plethora of professional opportunities available in this sector, the gap between qualified engineers and job positions available is still wide. Given the importance of this field, it is the best time to gain expertise and fine-tune your data science skills by enrolling in the relevant course and becoming the leaders in this field.
- What is Data Engineering? Who is a Data Engineer?
- The Path to Becoming a Successful Data Engineer in 2021
- Data Engineer Key Skills
- Data Engineer Jobs
- Are Data Engineers in high demand?
- Data Engineer Salary
- Should you become a Data Engineer?
- Conclusion
What is Data Engineering? Who is a Data Engineer?
Data Engineering is a broad discipline that encompasses multiple titles with a primary focus on creating reliable infrastructures to ensure consistent data flow in a data-driven environment. They are someone who acts as a facilitator of clean and raw data from various sources so that people can use it within the organization to make data-driven decisions.
Think of this individual as a farmer who takes care of the fields, maintains the soil and ensures that plants are healthy to harvest that crop for others. They are responsible for yielding high-quality crops.
This is similar to what data engineers do. That might sound simple and straightforward, but their task usually involves constructing, installing, testing and maintaining high-quality data to fulfil organizational goals.
Data Engineer Typical Duties include
- Exploratory data analysis
- Extracting data from a huge pool of unfiltered information
- Evaluation and cleaning of data sets
- Preparing and writing ETL logic
- Building data pipelines to distribute across multiple servers
- Mine and query data
- Stitch data together
- Create data stores
- Optimization of data delivery
- Data normalization and modelling
- Redesigning of data infrastructure for scalability
- Use the framework to serve data
- Assisting data scientists in optimizing products
The Path to Becoming a Successful Data Engineer in 2022
1. Earn a bachelor’s degree in a relevant field
Most data engineers possess a bachelor’s degree in science, mathematics or data-related field to prepare a strong base. By learning software engineering, you will be able to understand the basic concepts of programming and be able to get an entry-level job. You can take on projects to get real-world experience and create a diverse portfolio for future employment.
2. Master relevant skills
Fine tune your understanding of foundational programming languages, databases and big data skills. You can start by learning SQL basics as it is required to use SQL to query data. You should also learn how to model data, use database architectures, work with less structured data, construct data pipelines and undergo data mining. To further advance your skills, you can learn how to process big data in batches or streams. You can also learn about various tools such as Kafka, Hadoop, etc. to schedule workflows in the big data ecosystem.
3. Pursue additional certifications or courses
You will need additional professional certifications to advance your career in data engineering. You can either choose a Master’s degree with data engineering as a specialization or certificate courses that offer relevant technical skills such as automation, scripting, Java, Kafka, Tableau, distributed systems, to name a few.
4. Become proficient at programming
The industry requirements revolve around two major technologies – Python and Scala. To create good software, you might need to brush up skills in these languages and gain hands-on experience on data engineering tools. There are a lot of online courses offering certifications in these programming languages which are usually tool-specific. These certifications are recognised by employers for the relevant jobs. Having a firm grip on programming skills and languages such as Java, C++, Python and Scala are desired by employers.
5. Study cloud computing
It is crucial to know how to work seamlessly with cloud computing in the modern times. You can learn about different kinds of services provide by cloud platforms such as cloud storage, cluster management, data processing management, computation, etc.
6. Advance professionally
The best way to advance professionally is by keeping up with the latest trends and innovations in the field of data science. Keep an eye on new software launches and the upcoming data engineering tools. Work on more projects and build a portfolio to showcase your technical skills to land a good position in a reputed data engineering company.
7. Additional skills
To excel, technical skills are not enough. They should also be proficient in soft skills such as communication skills, presentation skills and collaboration skills, to name a few.
Data Engineer Key Skills
The key skills or competencies can be summarized as below –
- Programming language – Python, SQL, Java, etc.
- Databases – SQL and NoSQL based
- ETL/ELT Technologies – Apache Airflow, Hadoop
- Infrastructure -Cloud computing
- Streaming – Apache Beam
Data Engineer Jobs
The employment opportunities are ample and they are projected to increase by 15% between 2019 and 2029, according to a report by the Bureau of Labor Statistics. You can start taking your first step as a professional by starting as a software engineer and gain the necessary experience to follow this career path:
- Junior Data Engineer
- Data engineer
- Senior Data engineer
- Lead data engineer
- Head of data engineering
- Chief data officer
Are Data Engineers in high demand?
It would not be an exaggeration to say that data is the new oil. Data is everywhere, and the position of a Data Engineer is crucial to using the full potential of data in any organization. According to a report, it is one of the fastest-growing professions globally, witnessing over 88.3% growth in job postings in 2019 and over 50% year-over-growth in several open positions. They are poised to give tough competition to data scientists. The demand for data engineers has outpaced data scientists by 2:1 with a 20 to 30% increased payout than the latter. The salaries continue to grow rapidly year after year.
Data Engineer Salary
The biggest advantage of choosing this as a professional career is that it pays well. The average Data Engineer salary falls anywhere between $65,000 and $135,000 and it also depends on your educational qualifications, professional certifications, years of experience in the relevant field, additional skills, etc.
The annual salary for some of the top positions, according to the Bureau of Labor Statistics in 2019, were as following-
Database Administrator – $93,750
Computer Network Architects – $112,690
Computer Research Scientists – $112,840
According to Glassdoor, the estimated base salary for Data Engineers in 2020 was $102,864 annually.
According to the reports from Indeed.com, Data Engineers can earn up to $129,415 annually with an additional possible bonus of $5,000.
As of April 2021, the average Data Engineer salary in the US falls anywhere between $90,000 and $126,133.
It is obvious from these reports that this is one of the highest-paid talents in the industry and this trend is here to stay or must we say, evolve and grow in the near future.
Should you become a Data Engineer?
If you like cleaning up raw data and being a data wrangler and could work in a quiet environment, then data engineering is for you. Suppose you possess several technical skills and pursue a desire to learn more about programming languages or keep yourself up to date with the latest software developments. In that case, choosing a career in Data Engineering is the right choice for you. You will love the pay scale, job profile and responsibilities that come with it.
Conclusion
While it is easy to get an entry-level job, the hardest part is building your portfolio and experience. The substantial increase in cloud-based services by businesses has been one of the major reasons behind this soaring demand for data engineers. You don’t need to be an expert in all the fields and skills associated with data engineering. Simply pick one skill such as cloud platforms and gain hands-on experience by focussing on solving real-world problems that help showcase your talents in job interviews.
You can take up the PGP Data Science and Business Analytics Course and upskill today. The course offers online mentored learning and career support to help you power ahead your career. If you have any questions regarding the course, or the blog, please feel free to leave them in the comments below. We’ll get back to you at the earliest.
