How to Become a Data Engineer: Complete Roadmap

You need robust data management and analytics knowledge as a data engineer. Next, you must have experience working with programming languages and software development tools. Finally, you need to have excellent problem-solving skills and be able to analyze data. The following roadmap will help you get started on the data engineer roadmap 2022.

What is a Data Engineer?

A Data Engineer is a professional who helps manage and process big data. They work with software that helps them analyze data to find trends and correlations and then use this information to create models or systems that help companies make better decisions.

Data Engineer Roadmap 2022

● Level 0: Deciding to Become a Data Engineer

Infrastructure and architecture are data engineers’ focuses. The objective is to develop data and data systems for real-world usage. Data engineers free data from limitations and make it operational and analytical. Only then can data be used. This isn’t data science or data analytics. However, there’s overlap. Data engineers, scientists, and ML professionals collaborate on machine learning to create, construct, and train models.

Data Engineering Skills

And degrees and classes. Data engineering degree? Since many colleges provide widely acknowledged degrees for contemporary business, skipping formal education might be a mistake.

Depending on your job goals, take Online Data Science Course. Many data-related SMB and corporate positions need a degree, although you may freelance without one.

Consider coding boot camps. Math, statistics, and other ‘hard sciences’ are solid foundations for a data job.

● Level 1: Base Knowledge

First, master SQL and Python. Data engineers spend a lot of time using SQL to query databases. SQL helps with distributed systems, streaming, and NoSQL. Learn server fundamentals and Git. API and REST API knowledge is helpful. You should now understand how key computer technologies interact. Understanding organized vs. unstructured data is also crucial for machine learning. Data structures, data types, and algorithms are also helpful.

● Level 2: Develop Python Skills

Python is well-supported for data-centric development, and many data engineers and scientists utilize its libraries. Some of these libraries and frameworks aren’t for novices, but they’re lovely for learning Python’s depth and variety. Python is fast and easy to learn. It’s vital for AI and machine learning, and its automation frameworks are unmatched.

● Level 3: Your First Project

Data engineering projects help you apply knowledge. Building a REST API using Flask is a good data engineering project for beginners.

● Level 4: Data Warehousing and Pipelines

You should understand ETL pipelines by now. Learn about ETL services, CDPs, and cloud warehouses, which are part of the current data stack.

● Level 5: Data Modeling Project

Your second project should combine coding and warehousing for a company or organization.

● Level 6: Testing

CI/CD needs data engineers to test. Integration, functional, and unit tests must be well-understood. Test-driven development (TDD) abilities boost data engineer employment.

Unit testing tests individual modules without dependencies. One unit is tested to confirm correct operation. Integration testing tests components together.

Functional testing: Checks whether the software meets project requirements.

● Level 7: Advanced Cloud and NoSQL

Cloud capabilities have become crucial. If you’re interested in AI and machine learning, check out AWS and Google Cloud. NoSQL doesn’t support fixed schemas, normalization, and expressive queries like SQL.

● Level 8: Big Data, Streaming, and Distributed Systems

Streaming and distributed systems are easier to use. Data engineering in enterprise-level applications requires knowledge of distributed event streaming and Big Data frameworks. Use consumer data for eCommerce and other business objectives.

Spark, Kafka, and Hadoop are critical components; comprehending how they interact is vital. Learn about frequent real-time data streaming challenges. Schedule data pipelines and processes using Apache Airflow.

● Level 9: Learn Data Visualization and UI/UX

Data visualization, dashboarding, and UX/UX aren’t essential for most data engineering professions, but it’s good to learn as much data analysis and data science as possible.

Not all firms or organizations are data-mature. Therefore it’s ideal for broadening your data science understanding. At some point in your career, you’ll want to specialize in Big Data or machine learning.

● Level 10: Advanced Machine Learning

Machine learning is a cutting-edge data-centric field. ML beginners should be familiar with supervised, unsupervised, and reinforcement learning. Create a supervised and unsupervised machine learning project for your resume by learning through Online Data Science Training. Understand training and testing data, labeling, and annotation. Consider AI ethics, ground truth, and the obstacles we face in building AIs.

● Level 10+: Develop With The Field

Data engineering is a fast-growing discipline. It’s important to know what’s happening in the sector to boost your expertise and interest in data and to offer you something to speak about with clients, consumers, and employers. It’s equally important to discuss AI and ML ethics and compliance. Learn where the current data stack is headed.

Conclusion

This isn’t a complete list of data engineering abilities, and you couldn’t master them all in 2 to 5 years. In actuality, you’ll be adjusting your talents to meet demand. Once you find a career you like, learn about its abilities as much as possible. There are various chances for data engineers who want to learn cutting-edge skills and expertise.