Data Engineering as a Career

Data engineering has seen massive growth in recent years, surpassing even that of data science. From 2021 to 2022, data engineering job postings grew by 100%, compared to 68% for data science roles. It also has the 4th highest volume of job postings among tech roles.

The demand for data engineers remains high as long as businesses continue to use data for decision-making and answering business questions. This makes data engineering an excellent career choice with strong job security and growth potential.

Data engineer roles typically fall into four core groups:

1. Generalists: Involved in all aspects of data collection, storage, analysis, and movement. Often found in small companies or those in early stages of analytics.

2. Specialists in Data Storage: Responsible for setting up and managing databases, data warehouses, and other storage platforms.

3. Specialists in Programming and Pipelines: Create and manage the flow and movement of data, working with various programming languages and platforms.

4. Specialists in Analytics: Work closely with data scientists and other analytics professionals, supporting data-related projects.

Each group requires a specific set of skills and tools, allowing data engineers to specialize based on their interests and the needs of their organization.

Most data engineering positions typically require:

1. Qualifications: A bachelor's degree in computer science, engineering, mathematics, or a related IT field.

2. Certifications: While not always mandatory, certifications can help prove your understanding of specific frameworks or tools.

3. Experience: Many companies require at least a few years of experience in a related field or with relevant tools.

For those new to the field, it's common to start in a related role (such as software engineer or data analyst) and transition to data engineering after gaining experience.

While the field of data engineering is broad, these five skills are crucial for most roles:

1. SQL Skills: Master SQL and its various dialects for effective data manipulation.

2. Data Modeling Techniques: Learn to design scalable and optimized databases and warehouses.

3. Python Skills: Use Python for creating data pipelines, integrations, and data analysis.

4. Hadoop for Big Data: Work with big data using tools like Hadoop for scalable data processing.

5. AWS Cloud Services: Develop cloud computing skills, particularly with AWS services.

Additionally, soft skills such as problem-solving, teamwork, and effective communication are crucial for success in data engineering roles.