Analytics engineers are in high demand as organizations increasingly rely on data to drive decision-making. If you’re interested in becoming an analytics engineer, read on to learn what it takes to enter this exciting and growing field.
Analytics engineering is a relatively new field that combines the skills of software engineering and data analysis. As the name suggests, analytics engineers are responsible for building and maintaining the systems that enable organizations to collect, process, and analyze data.
With the increasing popularity of data-driven decision-making, there is a high demand for analytics engineers who can design and implement efficient data pipelines. If you’re interested in becoming an analytics engineer, here’s what you need to know.
What Does an Analytics Engineer Do?
In the past, data engineers were responsible for infrastructure and the extract and transformation jobs while data warehouse developers/BI developers focused on developing SQL scripts to do data transformations and maintain data warehouses. However, nowadays with the shift towards ELT, we no longer need data engineers to do data transformation for us. This allows very technical data experts such as BI analysts, data analysts or data warehouse developers to be fully in charge of data transformation jobs. ELT has many benefits, such as allowing businesses to be more agile and responsive to change and reducing the reliance on IT departments. It also enables businesses to make better use of their existing data assets and to easily integrate new data sources. Overall, ELT is a more efficient and effective way of managing data transformations, and it is now possible for businesses to get by without data engineers.
As data becomes increasingly important to businesses, the need for analytics engineers is also growing. Analytics engineers are responsible for designing and developing data warehouses and performing complex SQL queries. They also need to have a strong understanding of business and analytics in order to be able to identify business logic.
Traditionally, the ETL approach has required software engineers with a background in data analytics. However, with the shift to ELT, it is now easier to find people with strong SQL skills and a technical background who can fill this role. This is why an analytics engineer is becoming a very popular and much-needed role for a company.
Analytics engineers work with data scientists and other stakeholders to understand their needs and develop solutions that enable them to effectively analyze large data sets. In addition to technical skills, analytics engineers must also have strong problem-solving and communication skills.
How Do I Become an Analytics Engineer?
There is no one-size-fits-all answer to this question, as the best way to become an analytics engineer will vary depending on your background and experience. However, there are a few key things you can do to increase your chances of success in this field.
As an analytics engineer, you’ll be working with data transformations, debugging or optimizing SQL codes, and building and maintaining data warehouses and data models. Therefore, a good knowledge of SQL is required, as well as the fundamentals of data modeling. These are skills that you will constantly learn and use on your day-to-day tasks. Another fundamental skill is version control using tools like Git. Knowing how these tools work and how they are implemented will help you understand how other tools like dbt work. Version control also brings software engineering best practices to the table. Therefore, these are essential skills for any analytics engineer.
As a Analytics Engineer, it is important to have a strong understanding of the data orchestration process. This involves understanding how data is collected, transformed, and stored. Data Engineers typically handle this process, but it is still a good skill for Analytics Engineers to know. In addition, Analytics Engineers should be proficient in Python or at least have a strong understanding of the language’s concepts. Lastly, CI/CD is another good concept for Analytics Engineers to be aware of. CI/CD stands for Continuous Integration/Continuous Delivery and is responsible for managing the software development lifecycle. DevOps typically handles this process, but it is still a important part of the overall orchestration flow.
Now you might be wondering where I can learn all this information.
It’s hard to know where to start when learning analytics engineering. There’s so much information out there, and it can be overwhelming trying to figure out what you need to learn first.
Aecamp is the perfect platform for anyone looking to learn from industry experts, including real-life use cases and hands-on labs. You’ll have everything you need to get started with your first dbt project, gain a glimpse into the fascinating world of GCP, and if you’re not familiar with SQL we have a brand new course that will teach you all the fundamentals of SQL within BigQuery, using real-life examples and use cases.
With Aecamp, learning analytics engineering has never been easier. You’ll be able to learn at your own pace, in your own time, and with access to some of the best instructors in the business. There’s no better way to become an expert in analytics engineering than by using Aecamp.
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