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How to Become A Data Engineer?
Nowadays, modern businesses heavily depend on Big Data to meet its goals and objectives; that's why everybody wants to be a Data Scientist. But very few of them ever think about becoming a Data Engineer, a hybrid between a data analyst and a data scientist.
Attractive salary and high demand are negligible aspects of what makes this job to be most charming. Data Engineer's role needs essential technical skills, including in-depth knowledge of SQL databases, different programming languages and data science tools.
DATA ENGINEER IN A NUTSHELL:
Data engineers develop and maintain data workflows and stocking big data in an effective way so that it can be accessed later easily. A data engineer develops, manages, and tests data infrastructures, including databases and large-scale computing systems. The infrastructures and mechanisms that data engineers develop are then used for:
- Data modeling - To analyze data objects and their relationships with other data objects.
- Data mining - To study large pre-existing databases in order to predict upcoming trends and information.
- Data acquisition - To measure physical quantities like voltage, current, temperature or pressure using different devices.
- Data verification - To examine data for anomalies and inconsistencies after migration among two or more databases.
The data engineer works in collaboration with other data science job roles such as data architects, data analysts, and data scientists. Data architects are responsible for handling data management systems, and studying a company’s data usage, while data analysts translate data to possible solutions. In the end, data scientists implement machine learning and modern statistical handling. They share these insights with each other and related stakeholders through data visualization and effective presentation narrative.
RESPONSIBILITIES OF DATA ENGINEER:
The data engineer is mainly responsible for developing, constructing, testing, and handling data management systems. To conduct this chain of events, data engineers must have a proficient command over scripting languages and must be able to solve complex problems of programming and coding domain.
Keep in mind, and data engineers are the designers and developers of data management tools, not those who perform mining over data to study insights and trends. The data engineer thus serves as a playback artist and bound to maintain productive and effective communication with other team members who rely on his/her tools to propose or produce business solutions from big data.
- Design, develop, maintain, and test database and software systems.
- Construct data infrastructure for processing and reshaping data for large-scale applications.
- Handle data migration between two databases.
- Use different scripting languages, acknowledging the limitations, pros, and cons of each, to merge different models.
- Discover new methods to obtain data
- Discovering modern tools for existing data
- Collaborate with other team members, including data architects, data analysts, and data scientists
Data engineers must be proficient in using a wide range of technologies and programming or scripting languages. These tools and methodologies are constantly evolving, so one of the most essential needs that a data engineer has to fulfill is to stay updated with variations and know when to implement which language or tool and why. A superior data engineer must possess the following skills set and knowledge.
- Designing and Constructing large-scale applications
- Database infrastructures
- Data warehousing
- Data Modeling
- Data mining
- Statistical methodologies
- Distributed computing
- Design and Analysis of algorithms
- Expertise in implementing different programming and scripting languages, especially R, SAS, Python, C/C++, Ruby Perl, Java, and MatLab
- Domain-specific query languages, like SQL or Oracle, as well as Cassandra, and Bigtable
- Hadoop analytics tools to sustain big data, such as HBase, Hive, Pig, and MapReduce
- Operating systems, notably UNIX, Linux, and Solaris
- Machine learning libraries of python, such as Scikit-learn
- AI and computer vision tools for a .NET framework like AForge.NET
In brief, data engineers are expected to have a broad skill set of technical and programming expertise. Though a significant portion of the job requires critical thinking and problem-solving skills so that the right approach is used in the right direction depending upon the current situation and challenges.
Additionally, data engineers must be able to work in collaboration with other data science experts and share results and recommendations with team members having a weak technical background.
According to Payscale, the average salary of data engineers is around $90,286 per annum. Experience cast a notable effect on salary, as many data engineers occupying the roles for around 2 decades. The highest-paid data engineers have expertise in platforms like Scala, Apache Spark, Java, and different data modeling and warehousing methodologies.
DATA ENGINEER JOB OUTLOOK:
The tech firm Stitch reported a more substantial increase in data engineer jobs as compare to other data science job roles due to the apparent reason that efficient data infrastructure is mandatory for any facility that is up for utilizing data mining methodologies to gain useful insights.
Many data engineers debuting in this field hold a background in software engineering and make the transition with their existing skills in Linux, Java, SQL, Python, and Hadoop.
Due to the evolving nature of this career, data engineers can enjoy benefits by standing on the frontline of advancement related to data science and management.
MAINSTREAM DEGREE PROGRAMS:
The recommended programs include software engineering, computer science, or IT. As this job role demands more engineering skills, some engineering programs can also serve the purpose somehow. Regardless of your major, make sure to get yourself enroll in the courses of software engineering, computer programming, and database management systems.
Enhance your hands-on skills in computer programming and software design during the start of your career or internships. Because proficiency in different programming languages is crucial in sustaining this career. The more you gain experience, the more it will become easy for you to deal with real-world problems. This entry-level experience will help you a lot in letting employers know you have the required potential and the ability to become a data engineer.
Different Data Science Training sessions include essential aspects of Mathematics, Statistical data handling, Python, Advanced Statistics using Python, Machine Learning, deep learning, and their related libraries. There exist a variety of necessary to expert level certifications for data engineers willing to master their skills by attending Data Science Training sessions. The top 14 data engineer certification programs are as under:
- Amazon Web Services (AWS) certified big data specialty
- Cloudera Certified Associate (CCA) Spark and Hadoop Developer
- Cloudera Certified Professional (CCP)
- MapR Certified Hadoop Developer 1.0
- MapR Certified Spark Developer 2.1
- Google Professional Data Engineer
- HDP Apache Spark Developer
- SAS Certified Big Data Professional
- SAS Certified Data Scientist Using SAS 9
- HDP Certified Developer Big Data Hadoop
- Oracle Business Intelligence Foundation Suite 11 Certified Implementation Specialist
- IBM Certified Data Engineer
GRADUATE DEGREE PROGRAMS:
Soon after making a debut in your career, you will feel a need to obtain a master’s degree in computer science, data science, AI, or other relevant fields. There are many top-class universities offering MS programs in computer science in general and data science specifically.
After the resurrection of machine learning and recent breakthroughs in GPU technology, we have now different tools and language platforms to sustain and explore Big data, using programming skills and rich knowledge of mining and statistical methodologies. Data engineers are responsible for using these tools and constructing data infrastructure to hold and explore big data.
Although data scientists and data engineers have something in common that is their Computer Science backgrounds but both roles differ from each other in a sense that Data engineers are responsible for designing, building and testing state of the art data infrastructures whereas data scientists use these tools and discover insights and trends, hidden in an abyss of big data.