PS: This is a long post. Bookmark it and read at your ease.
I have been hiring in various capacities for data, database related roles. I have seen the roles and designations evolve along with technology. In the early 2000s Data Administrators were the most desired role by enterprises. Then came the era of Data Centers, where data center administrators, database experts, disaster recovery, backup, and storage were rockstars. Then the cloud took over from 2013 onwards, the size of data grew, and so did storage and processing capabilities. This demanded more AI, ML, mobile, IoT, and more visualizations, and thus came data pipelines and new technologies to support Data scientists, data analysts, business intel, etc.,
Fast forward to post covid era and 2023, Data Engineers will be the new need of the hour for the next 5 years to a decade. If you are one of those looking to transition or start learning or deciding to choose a career path, then Data engineering is both rewarding and challenging at the same time, albeit is a great choice.
Becoming a data engineer can be a good career choice in 2023 due to the growing demand, lucrative salaries, diverse job opportunities, challenging work, and room for growth in this field.
Here is a guide on how to go about becoming a great data engineer in 2023. Let's first identify all the skills you need, all the skills that recruiters look for in your CV/Resume, and finally let's also touch briefly upon how to stand out as a great data engineer.
To succeed as a senior data engineer in 2023, you would need a combination of technical, analytical, and soft skills.
Firstly, you should have strong programming skills in languages such as Python, Java, or Scala. You should also have experience with data processing frameworks such as Apache Spark or Apache Kafka.
Secondly, you should have experience in data modeling, database design, and query optimization, and have a good understanding of data warehousing concepts. You should have expertise in data integration and ETL (Extract, Transform, Load) processes to move and transform data from various sources into a unified format.
Thirdly, you should have knowledge of distributed systems and be familiar with technologies like Hadoop, MapReduce, and distributed computing. Knowledge of cloud platforms like AWS, GCP, or Azure is crucial, as cloud-based data engineering has become the norm.
Fourthly, you should be familiar with data visualization tools like Tableau, Power BI, or QlikView to create interactive dashboards and visualizations. Knowledge of machine learning concepts and techniques would also be helpful in developing data-driven solutions.
Lastly, as a data engineer, you should have strong communication, collaboration, and problem-solving skills, as well as the ability to mentor and lead a team. Being adaptable, staying up-to-date with the latest technologies, and having a passion for solving complex data problems are also essential skills for a senior data engineer.
Overall, succeeding as a senior data engineer in 2023 requires a diverse set of skills, including programming, database design, distributed systems, cloud computing, data integration, and soft skills like communication and problem-solving.
To become a senior data engineer, follow this roadmap with 30-day courses, dedicating 90 minutes daily.
Phase 1: Starting Point
Programming fundamentals: To start your data engineering journey, you should have a solid foundation in programming. Here are some free courses you can take:
Introduction to Python Programming by Udacity: https://www.udacity.com/course/introduction-to-python--ud1110
Java Programming Basics by Coursera: https://www.coursera.org/learn/java-programming-basics
SQL and database design: Next, you need to learn SQL and database design. Here are some free courses you can take:
SQL for Data Analysis by Udacity: https://www.udacity.com/course/sql-for-data-analysis--ud198
Database Management Essentials by Coursera: https://www.coursera.org/learn/database-management
Data warehousing and ETL: You need to understand data warehousing concepts and ETL (Extract, Transform, Load) processes. Here are some free courses you can take:
Data Warehousing for Business Intelligence by Coursera: https://www.coursera.org/learn/data-warehousing
Data Integration and ETL with Talend by Udemy: https://www.udemy.com/course/data-integration-with-talend/
Big data technologies: You should learn about distributed computing and big data technologies like Hadoop, Spark, and Kafka. Here are some free courses you can take:
Big Data Essentials: HDFS, MapReduce, and Spark RDD by Coursera: https://www.coursera.org/learn/big-data-essentials
Apache Kafka Series - Learn Apache Kafka for Beginners by Udemy: https://www.udemy.com/course/apache-kafka/
Apache Spark 3.0 with Scala: Hands-On with Big Data! by Udemy: https://www.udemy.com/course/apache-spark-with-scala-hands-on-with-big-data/
Cloud computing: You need to learn about cloud platforms like AWS, GCP, or Azure. Here are some free courses you can take:
AWS Cloud Practitioner Essentials by AWS Training and Certification: https://www.aws.training/Details/eLearning?id=60697
Google Cloud Platform Fundamentals: Core Infrastructure by Coursera: https://www.coursera.org/learn/gcp-fundamentals
Data visualization: Finally, you need to learn about data visualization tools like Tableau, Power BI, or QlikView. Here are some free courses you can take:
Tableau Fundamentals by Tableau: https://www.tableau.com/learn/training/elearning
Microsoft Power BI - A Complete Introduction by Udemy: https://www.udemy.com/course/power-bi-complete-introduction-beginner-to-advanced/
There are many other courses and resources available online to become a data engineer, but these should give you a good starting point. Remember to practice what you learn and build projects to showcase your skills. Good luck!
Advanced SQL: As a data engineer, you'll be working with large datasets, and having advanced SQL skills is essential. Here are some courses to help you level up your SQL skills:
Advanced SQL for Data Scientists by Udacity: https://www.udacity.com/course/advanced-sql-for-data-scientists--ud198
SQL Performance Tuning by Pluralsight: https://www.pluralsight.com/courses/sql-performance-tuning
Data processing frameworks: As a data engineer, you should be familiar with distributed processing frameworks like Apache Spark and Apache Flink. Here are some courses to help you master these frameworks:
Apache Spark Advanced Concepts by Udemy: https://www.udemy.com/course/apache-spark-advanced-concepts/
Building Real-Time Data Pipelines with Apache Kafka and Flink by Pluralsight: https://www.pluralsight.com/courses/building-real-time-data-pipelines-apache-kafka-flink
Machine learning and data science: Machine learning and data science are closely related to data engineering, and having knowledge in these areas can help you build better data-driven solutions. Here are some courses to help you get started:
Machine Learning by Coursera: https://www.coursera.org/learn/machine-learning
Data Science Methodology by IBM: https://www.coursera.org/learn/data-science-methodology
DevOps for data engineering: DevOps practices can help you streamline your data engineering processes and make them more efficient. Here are some courses to help you learn DevOps for data engineering:
DevOps for Data Engineers by Udacity: https://www.udacity.com/course/devops-for-data-engineers--ud611
DevOps Essentials by Pluralsight: https://www.pluralsight.com/courses/devops-essentials
Cloud certifications: Having cloud certifications can help you demonstrate your expertise in cloud-based data engineering solutions. Here are some certification courses you can take:
AWS Certified Big Data - Specialty Certification by AWS: https://aws.amazon.com/certification/certified-big-data-specialty/
Google Cloud Certified - Professional Data Engineer Certification by Google Cloud: https://cloud.google.com/certification/data-engineer
Phase 3: Standout & Mastery
Build a strong online presence: Create a professional website or online profile that showcases your data engineering skills, experience, and achievements. LinkedIn is a great platform for this. Be sure to highlight your skills and achievements in your profile, and include links to your portfolio, blog, or other relevant projects.
Create a portfolio of projects: Develop a portfolio of data engineering projects that demonstrate your skills and experience. Include a brief description of each project, the tools and technologies you used, and the problem you were solving. You can use platforms like GitHub or Kaggle to showcase your work. Here is an example of a data engineer's portfolio on GitHub: https://github.com/schumakl
Contribute to open-source projects: Contribute to open-source data engineering projects by fixing bugs, adding new features, or improving documentation. This is a great way to demonstrate your skills, gain experience, and build your reputation in the community. Check out the Apache Beam project for an example of an open-source data processing framework: https://beam.apache.org/
Participate in data engineering competitions: Participate in data engineering competitions and hackathons to challenge yourself, network with other data engineers, and showcase your skills. Platforms like Kaggle or HackerRank offer data engineering challenges. Here's an example of a data engineering competition on Kaggle: https://www.kaggle.com/c/titanic
Write technical blog posts: Write technical blog posts about data engineering concepts, tools, and techniques. This can help you establish yourself as an expert in the field and attract potential employers or clients. Here's an example of a data engineer's blog: https://www.dataengineeringpodcast.com/
Speak at conferences or events: Participate as a speaker or presenter at data engineering conferences or events. This can help you share your knowledge, network with other data engineers, and gain recognition in the field. Check out the Strata Data Conference for an example of a data engineering conference: https://conferences.oreilly.com/strata/strata-eu
Get certified: Obtain certifications in data engineering technologies like AWS, GCP, or Azure. This demonstrates your knowledge and expertise in the field and can set you apart from other candidates when applying for jobs. Here's an example of a certification program from AWS: https://aws.amazon.com/certification/certified-big-data-specialty/
Mentor others: Share your knowledge and expertise by mentoring others. Offer to mentor junior data engineers or participate in mentorship programs. This not only helps others grow their skills but also helps you deepen your own understanding of the field. Here's an example of a mentorship program for data engineers: https://www.datacouncil.ai/mentoring
By using these eight strategies, you can demonstrate your data engineering skills, build your reputation in the field, and stand out from the crowd.
