# Data Engineer Roadmap 2026: 3 Levels to Get Hired

## Метаданные

- **Канал:** techTFQ
- **YouTube:** https://www.youtube.com/watch?v=f1biHhDyoS4
- **Дата:** 07.01.2026
- **Длительность:** 10:43
- **Просмотры:** 30,766
- **Источник:** https://ekstraktznaniy.ru/video/52847

## Описание

In this video, you will learn the complete Data Engineer Roadmap for 2026, broken down into 3 clear levels: Hireable, Amazing, and Master.
Instead of getting lost in 20+ tools like SQL, Python, Snowflake, Databricks, Airflow, dbt, Kafka, PySpark, Docker, Kubernetes and more, you will see exactly which skills to learn first, which tools to prioritize, and which real-world projects to build at each stage.

Learn SQL from SQLNest 👇
https://sqlnest.com

Level 1 (Hireable) focuses on SQL, Python and one cloud data warehouse with a simple end‑to‑end project to get you job‑ready.
Level 2 (Amazing) adds data modeling, Airflow, dbt, CI/CD, Git/GitHub and cloud skills so you can design robust production pipelines.
Level 3 (Master) introduces PySpark, Kafka, lakehouse formats like Iceberg/Delta Lake, data quality frameworks, Docker, Kubernetes and Terraform so you can work on large‑scale, real‑time data platforms.

By the end of this video, you will know:
Which 20% of skills you actually need to 

## Транскрипт

### Segment 1 (00:00 - 05:00) []

SQL, Python, Snowflake, Airflow, DBT, Pispar, Kafka, Iceberg, Data Bricks, Data Modeling, AWS, and so many more. The question is, do you need to learn all of these skills to become a data engineer? And the answer is absolutely no. You only need about 20% of these skills to become a data engineer. In this video, I'm going to give you a complete road map of how to become a data engineer in 2026. Let's start with the basics. Who exactly is a data engineer? A data engineer is someone who is able to extract the data from multiple different sources, who is able to transform the data or prepare the data and then who is able to make that data available to the end users or to the stakeholders or to the business. Anyone who can perform these activities, you can call them as a data engineer. But then when you speak to a different bunch of data engineers, you would realize that most of the data engineers kind of perform these activities, but then they use different tools and technologies to perform these activities. And the reason for that is different companies have different tech stack. Different projects have different requirements. And based on that a data engineer might be heavily using one tool or the other. For example, you could find data engineers who perform most of their activities using Pispark. There could be data engineers who perform all of their activities just by using SQL and Python. And there could be data engineers who heavily use Snowflake for all of their data engineering needs. Now what this should basically tell you is that you cannot generalize a data engineering road map and that is why I have categorized a data engineer into three different levels. In the level one I have the hirable data engineer. These are the bare minimum requirements that you will need to become a data engineer. In the level two I have the amazing data engineer and level three are the master data engineers. Let's start by looking at the road map to become a hirable data engineer. Now when I say hireable data engineer, what I basically mean is this is the bare minimum requirement that you will need to have to become a data engineer. If you are a fresher, you want to get started with your career as a data engineer or you're working in some other field maybe nonIT or as an analyst or some other developer role and you want to become a data engineer or simply you want to apply for a data engineer role then these are the bare minimum requirements that you will need to have to become a hirable data engineer. Now as part of hirable data engineers there are only three skills that you will need to learn. SQL, Python and Snowflake. Now SQL and Python are non-negotiable. You cannot skip it. You will need to be good at SQL and Python both. When it comes to Snowflake, what I basically mean is you will need to be good at any one cloud-based data warehouse. It could be Snowflake data bricks, Google BigQuery, Amazon Red Shift or some other. Snowflake is something that I generally recommend. It's because it's one it's very easy to learn. Secondly, it's widely used and it's very popular. So a lot of companies are asking for snowflake skills. Now the reason why you will need to learn these three skills is because of the responsibilities that you would have as a data engineer. So you should be someone who is able to extract, transform and load the data from one source to another. And you should be able to load the data into a data warehouse, a cloud-based data warehouse mostly. And then once the data is there in the cloud-based data warehouse, you should know how to manage that data, build reports, optimize the data, make it available, etc. And that is why you need these three skills. Now to showcase that you have everything that is required to become a hirable data engineer, you will probably need to build one project and you will need to showcase that project on GitHub, resume, LinkedIn, etc. Now the project that you're going to build to become a hireable data engineer it needs to be an end to-end project where you start from extracting the data and you will need to extract the data from a few different sources. The sources could be let's say a CSV file or you will probably need to extract data using let's say a rest API or from some other databases and you probably can use Python to do this extraction. Once you have extracted the data you will need to transform that data. In order to do the transformation, you could probably use SQL and then you will need to load the data into a data warehouse. And the data warehouse probably you should choose Snowflake. Of course, you can choose some other data warehouse as well, but Snowflake is something that I would recommend. Once the data is present in Snowflake, you probably can use Snowflake to build some simple dashboards or build some reports. And this could kind of be your end to-end project. Not very complex, but I think it's very effective. And you can use all the three skills to showcase that you have these skills to become a hirable data engineer. Next up is level two, the amazing data engineer. An amazing data engineer should be able to use, of course, SQL, Python, and Snowflake or any other cloud-based data warehouse. But in addition to this, they should also be able to build a reliable data pipelines using tools such as Airflow for scheduleuling and orchestration. They should also be able to use DBT to manage SQL transformation, testing, documentation in a clean and modular way. They should be very good at data modeling. They should understand all the data modeling approaches, star schema, snowflake schema, slowly changing dimensions, etc. They should also be able to apply CI/CD to move data from a lower environment to a production

### Segment 2 (05:00 - 10:00) [5:00]

environment. They should be able to work confidently with any one of the major cloud providers like AWS, GCP or Azour. And finally, they should be able to collaborate very confidently with GitHub and Git. So these are some of the activities that an amazing data engineer should be able to perform. And in order to perform all of this, the skills that they will need to have is of course SQL, Python and a cloud-based data warehouse like Snowflake or Data Bricks or Google BigQuery or Amazon Red Shift. But in addition to that they should also be good with airflow, dbt, data modeling, CI/CD, git, github and any one cloud provider such as AWS, GCP or Azour. Now just mentioning these skills is not good enough in your resume. So you need to prove that you have you actually know these skills and you have actually used it. So you will need to build one advanced end-to-end data warehousing project. And the project that you're going to build should include some data modeling steps. So I would recommend that you start with designing a completely new data warehouse where you talk about star schema or some other approaches. You build some fact tables uh dimension tables and you design the whole data warehouse. Once you have the design ready then you start with the ingestion of the data. So you will need to inest the data from multiple sources. Uh it could be through rest APIs or from maybe from let's say S3 bucket from AWS or from some other databases etc. where you will need to use Python. Once you have extracted the data, you will need to load the data into let's say a raw schema in the cloud data warehouse let's say snowflake and then you will need to move that data into a transformation schema where you can do the transformation and calculations and everything else using SQL and then you will need to move that data into let's say some mini data marks or some reporting schemas. Now all of these different steps you will need to orchestrate using airflow where you do the injection. You will need to use some dbt models for SQL transformations, some quality checks etc. You will also need to implement a simple CI/CD pipeline where any changes in your GitHub could trigger some test and deployment into your environment. If you can build a robust and advanced project like this, then you are ready to become an amazing data engineer. Finally, let's talk about level three, the master data engineer. At this level, you should be familiar with everything that we discussed in level one and level two. But in addition to that, you should also be able to design and build distributed data processing systems using Pispark. Implement realtime streaming using Kafka or similar tools. You should be able to set up data quality and testing frameworks using great expectations or similar frameworks. Understand DevOps practices well enough. So you will be able to containerize and deploy data workloads using docker, kubernetes and terraform for infrastructure. And finally you should also be able to work with lakehouse table format such as iceberg or delta lakes to power large scale analytics. Now the skills that you will need to add on in addition to the skills that you have already captured from level one and level two are you will need to be good with pispark kafka uh great expectations frameworks. You also need to have understanding of Docker, Kubernetes, Terraform and also Delta Lake as well as iceberg. Finally, you will need to build one solid project which will showcase that you have all the skills to become a master data engineer. And this project needs to not only include batch data processing but also live data streaming processing. So your project should ideally should start with let's say a complete end- to-end system design where you show the entire architecture which should include the batch sources uh the streaming sources Kafka topics processing layer storage as well as the final data analytics warehouse. In this project, you will need to showcase how you're going to ingest streaming data through Kafka and process it into the lakehouse either Delta Lake or iceberg and also showcase the batch loads. You will need to use Spicepark for all the heavy transformations and aggregations. You will also need to model your final data warehouse either using star schema or some other modeling approaches and also show how it connects to other BI analytic tools. Make sure that you orchestrate the whole project using airflow. Use dbt wherever SQL transformation makes sense and integrate great expectations framework for some validations. Additionally, you can containerize parts of the pipeline using docker. You can optionally orchestrate using kubernetes and probably manage infra using terraform if possible. If you can build a project using all of this or maybe most of this, then you would have a very solid project to showcase that you are ready to be a master data engineer. I hope the video was useful and you found some information about how you can become a data engineer. The important thing to note is you don't need to start with Pispark, iceberg, Kafka, etc. You need to start from the basics, SQL, Python and Snowflake. If you know that you can become a data engineer once you become a data engineer, you can then upskill and

### Segment 3 (10:00 - 10:00) [10:00]

learn other things to basically become a master or an amazing data engineer. Now, one thing that you might have noticed is that SQL and Python are kind of like the fundamental skills that you will need to become a data engineer. And if you are interested in learning SQL, I would highly recommend you to learn SQL from my own platform that is SQL Nest. It's a platform which will give you everything that you need to know about SQL. We have amazing courses. We also have interview problems that you can solve live on the platform. There are also case studies where you can solve SQL in the form of a project. You also have mock test, SQL challenges and much more. So definitely check out SQL Nest. You'll find the link in the video description. Thank you so much for watching and I'm going to see you soon next week.