9 LPA to 30 LPA: My Practical Data Engineering Roadmap Part-1 | Career Switch #dataengineering

Summary

In this video, the speaker shares a roadmap for transitioning into data engineering, particularly for those from outdated technologies or non-tech roles. After successfully switching to a data engineering role with a significant salary increase, the speaker outlines key steps for success: setting a clear goal, dedicating time daily, understanding interview structures, learning essential skills like SQL, Python, and Spark, and tackling important concepts such as airflow, data warehousing, and cloud technologies. By following this structured approach, viewers can confidently land a data engineering job.

Takeaways

• 😀 Focus on building enough skills to crack data engineering interviews, rather than trying to become an expert in the field.
• 😀 Set a clear deadline for when you want to secure a data engineering job to stay focused and motivated.
• 😀 Dedicate at least two hours daily for consistent learning and preparation over the next 3-6 months.
• 😀 Understand the general structure of data engineering interviews, including SQL, Python, Spark, and managerial rounds.
• 😀 Prepare for the technical rounds (L1) with SQL, Python, and Spark, focusing on theory and basic coding problems.
• 😀 Don’t over-focus on DSA; concentrate on solving easy problems in SQL and Python that are likely to come up in interviews.
• 😀 In-depth knowledge of Spark’s architecture and optimization techniques is essential for L2 interviews and data engineering roles.
• 😀 Gain practical experience by completing a solid real-time project that showcases your skills and can be discussed during interviews.
• 😀 Reading real-world articles about how top companies like Uber, Amazon, and Netflix solve data problems helps you provide practical answers in L2 and L3 interviews.
• 😀 Build your knowledge in areas such as Airflow, data warehousing concepts, cloud technologies (like AWS), and orchestration tools for a well-rounded skill set.

Q & A

What is the main objective of this video script?

The main objective of the video is to guide people transitioning into data engineering roles, especially those from outdated technologies, non-tech roles, or freshers trying to break into the field. The script provides a roadmap for getting hired as a data engineer by covering essential skills and interview strategies.

What mindset should someone have when preparing for a data engineering job?

The mindset should be focused on learning enough skills to crack data engineering interviews, not necessarily becoming an expert in the field. The goal is to gain enough knowledge to pass the interview and secure a job, rather than aiming for complete expertise in the field.

Why is it important to set a clear deadline for landing a data engineering job?

Setting a clear deadline ensures focus and motivation. Without a defined goal, it's easy to keep postponing the job search. A fixed timeline helps in structuring preparation and tracking progress towards achieving the goal of securing a job.

What is the recommended daily time commitment for preparing for data engineering interviews?

It is recommended to dedicate at least two hours daily, either in the morning or at night, for consistent preparation over the course of 3 to 6 months.

What does the typical interview structure for data engineering jobs look like?

The interview process consists of multiple rounds: L1 (technical screening focusing on SQL, Python, and Spark), L2 (deep dive into data engineering concepts like Spark optimization and data warehousing), L3 (managerial round focusing on project experience and team management), and HR (final round focusing on negotiation and location constraints).

What are the key areas to focus on when learning SQL for data engineering roles?

Focus on both theory and problem-solving. It's recommended to spend time understanding SQL concepts through courses (e.g., Udemy) and practice solving SQL problems on platforms like LeetCode. A solid grasp of SQL is crucial as it can make or break an interview.

How should one approach learning Python for data engineering?

Python knowledge should be basic and focused on scripting for data engineering tasks. There’s no need to dive deep into advanced topics like classes and objects. Practicing basic concepts like arrays and strings is sufficient, and learning through challenges like the '100 days of code' is suggested.

What is the importance of learning Spark for a data engineering role?

Spark is crucial for big data processing, and it's essential to learn both the theory (architecture, optimization) and the coding syntax for Spark. A month of focused study on Spark, including theory and coding exercises, is recommended.

What is the recommended approach for learning about data warehousing?

It's important to understand fundamental data warehousing concepts like facts, dimensions, star and snowflake schemas, and slowly changing dimensions. Knowledge of platforms like BigQuery, Redshift, and Snowflake will also be beneficial.

What role does cloud technology play in a data engineering job, and which cloud platform is recommended?

Cloud technology is essential for data storage, processing, and orchestration. The video recommends learning AWS as it's a market leader, and gaining hands-on experience with AWS data engineering services such as Glue, Lambda, and S3 is beneficial for building practical expertise.