Looking to start a career in information technology? Our complete Snowflake Data Engineer roadmap will give you an overview of the top reasons to become a Data Engineer, as well as actionable steps to kick-start your journey.
Use the jump links below to navigate to the section of our Data Engineer roadmap that most interests you:
Why are Data Engineers in demand?
Everyone is talking about the rise of AI services and applications, but data is the world’s most valuable resource right now. Because even the smartest AI algorithm or most advanced application still relies on robust data sets to deliver its results. With businesses clamouring to embed AI services into their operations and generate value from this exploding technology, they need the right tools and the right people to manage and operate their data sets effectively. Which is why Snowflake Data Engineers are so invaluable to businesses all around the globe.
Quick overview: what is Snowflake?
Snowflake is one of the most popular data management tools. A global network where thousands of organizations mobilize data with near unlimited scale, concurrency, and performance, Snowflake Data Cloud allows users to store, manage, and analyze huge amounts of data. It makes collecting and processing data much easier, and helps organizations gather useful insights to drive powerful data-driven decision-making.
But to make the most of a tool like Snowflake, you need a skilled professional at the controls—and that’s where Data Engineers come in.
Businesses are investing heavily in their data strategies, as well as groundbreaking AI applications and services, generating massive demand for Data Engineers who can help them manage, analyze, and optimize their data and provide better services. And that demand is creating fantastic opportunities for anyone looking to build an exciting and lucrative career in technology.
What are Data Engineer jobs like?
As a general overview, Data Engineer jobs entail designing, building, and maintaining any data-related architecture within a business. As the go-to data experts in their organization, Data Engineers use their expertise to determine the best way to collect, store, and process data, so that the business can get the most value from it.
That includes optimizing data pipelines, as well as developing data governance and security processes to ensure that data is secure at all times.
Data Engineers will also have a hand in data hygiene, analyzing data from various sources to make sure it’s accurate, reliable, and ready for use by other colleagues including Data Scientists and Data Analysts.
In short, Data Engineer jobs entail being the keeper of the data, tasked with managing all data efficiently, effectively, and securely.
Why become a Snowflake Data Engineer?
In today’s business landscape, data is king. We create mind-blowing amounts of data every single day. And as we digitalize more and more of our lives, these numbers are only set to grow.
It’s estimated that we’ll generate 181 zettabytes of data a year by 2025—to put that in perspective, the world generated just two zettabytes in 2010. Businesses need people with data skills to help them manage these colossal data streams, cherry-pick valuable insights that will help their organization grow and provide better services to their clients.
That’s why there’s never been a better time to start a career as a Snowflake Data Engineer. Let’s drill down a little further into why a career as a Snowflake Data Engineer is such a great choice.
Skyrocketing demand: Snowflake is expected to grow its total available market from $140 billion in 2022 to $290 billion by 2027. As more companies migrate their data to Snowflake, the demand for skilled Snowflake Data Engineers who can manage the platform and its applications increases as well, making Snowflake jobs highly desirable.
High salaries: Working with Snowflake requires a number of specialist skills, including SQL, ETL, and data warehousing. The complexity and rarity of these skills equate to lucrative Data Engineer salaries. This is especially true when combined with proficiency of the Snowflake platform, with bigger compensation packages for Snowflake jobs on offer compared to those extended to generalist Data Engineer jobs.
Access to cutting-edge tech: Snowflake is known for its innovative architecture and smart features designed to make data warehousing and analytics more efficient and scalable. As a Snowflake Data Engineer, you’ll get hands-on experience with emerging technology and practices in the data engineering field, which is partly what makes Snowflake jobs so popular.
Good career growth opportunities: As organizations continue to invest in data analytics and Snowflake increases its customer base, Snowflake Data Engineers are likely to become even more critical. Snowflake teams will grow larger, more Snowflake jobs will become available, and more opportunities for growth and development will crop up in the process to further optimize a range of services and applications offered by Snowflake as they emerge. By specializing in Snowflake, you can put yourself in a great position to capitalize on these opportunities in the data engineering domain.
Career stability: There’s no such thing as a future-proof career but given the world’s increasing reliance on data and AI, it’s extremely unlikely that Data Engineer jobs will become obsolete anytime soon. With the right expertise and experience, you can set yourself up for a long career in this exciting, ever-evolving data space.
Versatility: Since Snowflake is a versatile platform designed to work with countless data sources and tools, you’ll encounter various types of data pipelines, data lakes, and data warehouses. This variety will allow you to work on a wide range of data projects across different industries as a Snowflake Data Engineer, so you can keep things interesting.
Average Snowflake Data Engineer salary
Snowflake Data Engineer salary varies depending on your experience, certification levels, and location. But as an estimate, the average base salary for a Snowflake Data Engineer in the UK is around £50,080 per year. In the United States, you can expect to earn around $98,000 on average.
Snowflake Data Engineer role and responsibilities
Snowflake Data Engineer jobs entail architecting and implementing large-scale data intelligence solutions using the Snowflake Data Cloud. But what does that look like on a day-to-day basis?
Here are a few things a Snowflake Data Engineer might do during their working day:
Designing data pipelines: Snowflake Data Cloud uses data pipelines to ingest data into its system from sources like databases, cloud storage, or streaming platforms. A Snowflake Data Engineer designs, builds, and fine-tunes these pipelines to make sure that all data is loaded into Snowflake correctly.
Data modeling: Once this data is loaded into Snowflake, a Data Engineer designs and implements data models to make sure the data is stored efficiently, so that it can be easily retrieved and analyzed.
Performance tuning: Any cloud platform should be constantly monitored to make sure it’s running at the top of its game, and Snowflake is no exception. Data Engineers regularly review the performance of their Snowflake data warehouses and queries. They can then carry out optimization tasks that ensure efficient data processing.
Data integration: Snowflake works well alongside other tools, helping boost interoperability and give businesses the most comprehensive view of the data coming in and out of their environment. A Data Engineer will work to integrate Snowflake with other systems within the data ecosystem, setting up connections and configuring APIs to allow data to flow seamlessly between platforms.
Security and compliance: Data security is of the utmost importance in any business. A Data Engineer uses their knowledge on this front to roll out security measures and data governance best practices, and ensure everyone that comes into contact with data has a general overview of these and is adhering to them. This helps guarantee that data stored in Snowflake is secure, compliant with regulations, and accessible only to authorized users.
Collaboration: Data Engineers work closely with all kinds of stakeholders, from Data Scientists and Analysts to non-technical business leaders. By liaising with people across the business, the Data Engineer can better understand data requirements, provide relevant support, and pull actionable insights from Snowflake data.
Troubleshooting: Data Engineers are at the first port of call when it comes to identifying and resolving data-related issues, performance bottlenecks, and data quality issues within Snowflake data pipelines and warehouses.
Documentation: A good Data Engineer will document everything they do so they can refer back to it or enable others to develop an understanding of the data infrastructure. They’ll spend time recording their data engineering processes, data flows, and data models within Snowflake, ensuring the clarity and maintainability of the data infrastructure.
Continuous learning: Data management is a fast-evolving field. Data Engineers need to stay up to date not only with industry best practices, but also with updates to the Snowflake platform itself to constantly improve their data engineering skills and optimize data processes.
Project management: Data Engineers also need to keep track of their various data engineering projects. That might mean checking in on timelines, reviewing priorities, and delivering updates to stakeholders so that everyone is on the same page as they work to meet business objectives and requirements.
Do you need to complete a Snowflake Data Engineer course?
Data Engineer courses and certifications are an unbeatable way to build, advance, and showcase your Snowflake knowledge as a Data Engineer.
You don’t strictly have to have cloud certifications to get a job as a Snowflake Engineer, but if you’re just starting out and don’t have a lot of practical experience, certifications or a Data Engineer course are a good way of proving that you have the knowledge needed to do the job.
The real value in these certifications isn’t just in proof of expertise though—it’s in the range of skills that certifications can equip you with. By taking on these accreditations, you’ll build a solid foundation of valuable skills needed to succeed as a Data Engineer. You’ll get to learn what tools you need in your professional toolbox and what other tools are available at your disposal. And if you choose to get certified through Revolent, our range of hands-on exercises will help you gain a better understanding of when to use these tools in your new role and why.
Plus, having certifications on your résumé can open up new professional opportunities. According to a recent Snowflake study, 35% of certified Snowflake professionals said that earning a certification had increased their job opportunities, and 61% said they’d experienced a greater demand for their skills since becoming SnowPro-certified.
There’s a lot of learning material out there, so make sure you look for high-quality Data Engineer courses (like our Snowflake Data Engineer Careers Program, built in close collaboration with Snowflake) or check out Snowflake’s SnowPro certifications, such as the SnowPro Core Certification for budding Data Engineers.
Did you know that people who train with Revolent have a 90% first-time SnowPro certification pass rate? (That’s way above the industry average.) Find out why.
How do I get started as a Snowflake Data Engineer?
If a career as a Snowflake Data Engineer sounds like it could be for you, you may be wondering how to get started on your training journey. Here are some steps you can take right now to kick off the process!
1. Learn the basics
There are several concepts that underpin the work of a Data Engineer, so you should start by familiarizing yourself with these fundamentals. Learn data engineering, data warehousing concepts, SQL, and cloud computing (there are lots of free courses out there that can help) and you’ll build a strong foundation for working with Snowflake. Learning a programming language, such as Python, is also recommended.
2. Get hands-on experience
No amount of training can beat getting hands-on with a platform and getting to know it at your own pace. Sign up for a Snowflake free trial or use a demo account to poke around and see what it can do. You could try setting up data warehouses, loading data, writing queries, or performing basic data transformation to get some practical experience. Or why not register for our Snowflake Data Engineer Careers Program, inclusive of a full-time salary and a work placement with one of our market-leading clients?
3. Take an accredited Data Engineer course
Snowflake offers many training courses and certification programs, and a lot of these are completely free and self-paced. Enrol in cloud training courses that cover various aspects of Snowflake architecture, features, and best practices to round out your knowledge. Alternatively, check out our fully-funded Snowflake Data Engineer Careers Program.
4. Start your own projects
Once you know your way around Snowflake, you can start working on your own data engineering projects. You could start by building data pipelines, designing data models, or optimizing query performance, which will further accelerate your training journey with vital skills.
5. Join online communities
The Snowflake community is a useful, friendly place to get tips and advice to boost your training journey. Tap into these online resources by joining forums and discussion groups dedicated to Snowflake. Connecting with other Snowflake users can help you learn from their first-hand experiences as Data Engineers and stay updated on the latest Snowflake trends.
6. Attend webinars and workshops
There are plenty of webinars, workshops, and other events related to Snowflake and data engineering. These events offer some great opportunities to learn from experts, network with professionals in the field, and gain insights into best practices. Check out all upcoming Snowflake webinars here.
How to land your first role as a Snowflake Data Engineer
Ready to start your career as a Snowflake Date Engineer?
At Revolent, we’ll put you on the fast track to success with our fully-funded Snowflake-accredited Data Engineer Careers Program, inclusive of SnowPro certifications, soft skills training, and a paid placement with market-leading companies.
When you join our Data Engineer Careers Program, you’ll earn while you learn, receiving best-in-class training, fully-funded SnowPro certifications, and ongoing development opportunities for the duration of your work placement.
All you need is at least one year of basic programming knowledge (preferably in Python), familiarity with version control tools (Git would be best), and basic knowledge of SQL.
Start your journey to becoming a certified Snowflake Data Engineer today!
