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Big data is big business. With 90% of enterprise analysts and business professionals saying data and analytics are the key to digital transformation initiatives, the demand for skilled professionals continues to increase. 

Big data can have a big impact for businesses —  not only can they save money and time, they can analyze customer trends to develop new products and better understand market conditions. To make the most of this data, companies need the right people with the right skills in the right roles, including data engineers.

Becoming a data engineer — and more specifically a Google Data Engineer —  offers a way to capitalize on this career opportunity. Here are the details to help you get started.

What’s a Google Data Engineer?

Data engineering is the practice of transforming raw data into a more usable format so it can then be analyzed. Cleaning, organizing, and manipulating data through the use of pipelines are just a few of the tasks that data engineers oversee. 

As a Google Data Engineer, you’d focus on applying the principles of data engineering through the Google Cloud Platform

The Google Cloud Platform is highly secure, flexible, and affordable, making it one of the most widely used platforms. With nearly nine billion in revenue in 2019, (which is more than double 2017 revenue), adoption is growing exponentially, and resulting in increased demand for Google Data Engineers.

Background and Education

To land a role as a Google Data Engineer, you’ll need a degree in Computer Science, Statistics, Informatics, Information Systems, or another quantitative field. Then, to become certified as a Google Data Engineer, you must take a two-hour exam that measures your ability to design, build, and operationalize data processing systems, run machine learning models, and ensure solution quality. 

Keep in mind that these roles require a comprehensive understanding of data structures and algorithms, cloud platforms, SQL, Python and Java programming, data pipelines, distribution systems, and parallel programming. You can also expect employers to be looking for familiarity with common big data tools such as Hadoop, Spark and Kafka.

Roles and Responsibilities

As a Google Data Engineer you can expect to do the following on an ongoing basis: 

  • Design, build, operationalize, secure, and monitor data processing systems in the Google Cloud Platform. 
  • Leverage, deploy, and continuously train pre-existing machine learning models.
  • Identify, design, and implement internal process improvements.
  • Automate manual processes to optimize data delivery.  
  • Architect distributed systems and data stores. 
  • Combine data sources and create reliable pipelines.
  • Collaborate with data science teams and key stakeholders to meet business objectives.

Take Your First Step Towards Becoming a Google Data Engineer

The big data market, including data engineering, continues to grow in both volume and complexity. This can be attributed to ever-increasing mobile data traffic, cloud computing traffic, and the burgeoning development and adoption of technologies including artificial intelligence and IoT.

With the right set of skills, you can position yourself to land the job of your dreams working with big data as a Google Data Engineer.

Ready to start building the skills you need to become a Google Data Engineer?

The Udacity Data Engineer Nanodegree is designed to teach you how to design data models, build data warehouses and data lakes, automate data pipelines, work with massive datasets, and solidify your new skills with a final capstone project.

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