Why Become a Data Engineer?Data engineers are the need of the hour. They are an integral part of a company’s data strategy because the velocity, volume, and variety with which we are producing data are increasing rapidly. By the end of 2025, more than 180 zettabytes of data will be created, captured, and consumed. We need data engineers to handle such a huge amount of raw data. With such high demand, it offers a promising career in the data ecosystem.
Responsibilities of a Data EngineerA data engineer’s job is to understand the organization’s data requirements and build systems to provide clean, accessible data. On a day-to-day basis, they perform the following tasks:
- Designing, building, and maintaining the data pipelines
- Working with data analysts and scientists to better understand the data requirements
- Validating data sources and focusing on data quality
- Ensuring compliance with data regulations
How to Become a Data Engineer?The roadmap to becoming a data engineer is as follows:
1) Acquiring Relevant Data Engineering Skills
According to an analysis of 17,000 data engineer job postings, more than 70% of recruiters seek candidates proficient in Python and SQL. Hence, learning Python and SQL should be the first step to becoming a data engineer. Moreover, familiarity with other programming languages, such as Scala and Java, can give you a competitive advantage.
b) ETL (Extract, Transform, Load)
ETL means extracting data from various sources to single storage, transforming it into a form intended for analysis, and loading it into a data warehouse. Creating and maintaining ETL pipelines is a data engineer’s responsibility. Hence, learning ETL tools such as Integrate and Talend is necessary for data engineering.
c) Data Storage Systems
Databases are used to store the gathered data. Familiarity with relational, NoSQL, and data lakes as different data storage types is essential.
d) Big Data Tools
Understanding big data tools such as Apache Spark, Apache Hadoop, and Apache Hive is necessary for becoming a data engineer. These tools are used for processing, storing, and querying large volumes of data.
e) Cloud Computing
Cloud providers such as AWS (Amazon Web Services) and Microsoft Azure provide scalable computational resources for data storage and processing. Cloud computing certifications can help you learn and practice the fundamental and advanced concepts of various cloud platforms.
f) Soft Skills
A data engineer should have good communication skills to collaborate with other team members, including data scientists and data analysts. Creativity and problem-solving can help solve challenges in the data engineering lifecycle.
2) Getting CertificationCertifications enhance credibility and gain your employer’s trust. Data engineering certifications can be acquired from credible educational platforms like Coursera and Udemy. They have a high-quality practical curriculum taught by skilled educators. But, read course and instructor reviews before registering yourself. You can also visit the LinkedIn profiles of professional data engineers to find out which certifications they have acquired. It will give you a better understanding of which tools or platforms are currently trending in the industry.
3) Building Your Data Engineering PortfolioA portfolio is one of the best metrics to assess a candidate’s understanding of the subject. Creating multiple projects related to database design and development can distinguish you from other applicants. Uploading your data engineering project on GitHub and sharing a walkthrough blog post on platforms such as LinkedIn or Medium is an important step to showcasing your data skills.
4) Securing an Entry-Level Data Engineering JobIn most cases, data engineering is not an entry-level position. Getting an entry-level job as a data analyst can be a good start. As you gain more experience and skills, you can work up to a data engineer position.
Major Differences Between a Data Engineer & a Data ScientistAlthough there are some similarities between the skills and tools used by data scientists and data engineers, there are some distinct differences between them which are as follows:
|Making data infrastructures (data warehouses, data lakes, etc.) for data analysis is the key responsibility of a data engineer
|A data scientist is responsible for finding hidden patterns, building models, and making predictions on unseen data
|Expertise in database design and ETL processes using Python, SQL, and Java
|Proficient in data visualization, statistical analysis, and machine learning using Python or R
|SQL Databases, MongoDB, Apache Spark, Apache Hadoop, and Cloud Platforms (AWS, GCP, etc.)
|Pandas, Scikit-Learn, Tableau, PyTorch/TensorFlow, and Cloud Platforms
|To provide high-quality, accessible data
|Solve complex business problems and help companies make data-driven decisions