How To Level Up in Your Data Engineering Role

Data engineering is one of the fastest-growing jobs in tech. And that isn’t surprising given the value — and sheer amount — of data that companies are capturing today. IDC forecasts that by the end of 2025, more than 175 zettabytes of data will be created, captured, and consumed – up from 33 zettabytes in 2018. Many companies have access to vast, growing amounts of data sitting in warehouses or customer data platforms (CDPs) but still don’t truly understand the potential value of their data. Companies can’t afford to lose these critical insights when it comes to making informed business decisions. Enter Data engineers. Landing a role in data engineering isn’t just about technical acumen. Of course, technical skills are a critical piece of the role, but to become a great data engineer, you need to understand the bigger picture of what problem you are trying to solve. Here’s how to get the role — and grow in your data engineering career.

Learn the Language of Data 

Data engineers design, manage, and optimize the flow of data within an organization’s databases and develop data pipelines for analytics. But if you zoom out, data engineers are more than data shepherds. They are enablers in the organization, giving self-serve data access to the rest of the company. They can derive a single source of truth from complex, evolving datasets, working with data science and line of business teams such as marketing and product functions to help promote data-driven decision-making within an organization. Essentially, data engineers act as the translator of data to non-technical teams. 

While there’s no perfect roadmap or course of study to pursue to become a data engineer, a few must-have traits and qualifications will set you up for success. A data engineer should have the following:    

• Proficiency in programming languages such as Python, Java, and C++.
• Effective data model design skills that accurately represent the structure and relationships of data.
• Database and data warehousing skills, including experience with SQL or NoSQL databases or data lakes. 
• Experience with the Extract, Transform, and Load (ETL) or Extract, Load, Transform (ELT) processes to integrate data from different sources.
• Expertise in data governance, data pipeline, and workflow management.
• Understanding of modern data stacks and experience with tools that are used by specific teams (e.g., finance, HR) to gain insight into data. 

While you can study these skills and models, there is no replacement for experience. This can help budding data engineers understand the differences between a data warehouse and a data lake or the difference between SQL and NoSQL databases. These skills tend to ebb and flow as new platforms and ways of optimizing data are developed, so always keep an open mind and remain willing to learn new things as the industry continues to evolve. 

Don’t Overlook Your Soft Skills Development 

In addition to having the right technical tools in your toolbox to become a data engineer, soft skills such as communication, empathy, and collaboration are essential for success. It is very important to work closely with the line of business and understand what insights are important for them and why, so you can figure out how to create systems to enable them to self-serve those insights with speed and accuracy. I like to say data engineers play the role of the Great Translator — translating data insights for the teams, customers, and partners who need to understand them. Organizations rely heavily on data to understand what’s working and what’s not, both internally and externally, so it’s critical to communicate insights clearly and effectively. If you’re deciding between a career in software engineering or data engineering, I recommend asking yourself the question: do I want to head down the building, or am I energized by cross-functional communication and collaboration? Let your response guide you in your engineering career path.

Empathy is also an important soft skill for data engineers to hone. Data engineers who don’t spend time talking with their customers miss an opportunity not only to solve complex problems faster but also to gain a better understanding of their customer’s business goals. With that knowledge, you can better serve your customers proactively and create a true partnership rather than just a vendor-client relationship.

Finally, data engineers should prioritize collaboration. At Amplitude, we believe that product analytics is a team sport, where everyone from the product function to our data scientists and data engineers works together to solve problems and innovate as a unit. When data engineers share insights with the various cross-functional teams they support, early collaboration on the goals of the product ensures that the right insights are delivered and accurate goals are set.

Put on Your Founder’s Hat

I believe that the role of engineering within an organization is to increase the company’s capacity to win. It may sound daunting, but increasing your capacity to win can be broken down into lots of little (or big!) decisions. In making these decisions, my advice is always the same: put on your founder’s hat. I once had to learn this lesson myself. Years ago, I was faced with an engineering decision, and I outlined both options to my then-boss. He looked at me and asked, “If this was your company, what would you do?” I told him if I were CEO, I’d do option one, and that was the end of the discussion. It was a powerful perspective shift for me. Today, when I’m hiring, I look for people with this same mentality. Putting on your founder’s hat means you’re thinking business-first, not just engineering-first — and this is an important mindset for advancing your data engineering career. 

In a more recent example, I work with an engineering manager who is excellent at building products. They take ownership of readying the product for go-to-market and its success and have even created their own dashboard within Salesforce to understand which customers could find value in the product they are creating. In addition, they schedule a time to reach out. This is out of the normal realm of what a data engineer does daily and is a great example of pairing communication, empathy, and collaboration with technical chops. By thinking about how the product can be successful and taking into account the market and customer interest, they are looking to make the entire organization successful. They are wearing their founder's hat. 

I love Zig Ziglar’s quote, “Success occurs when opportunity meets preparation.” I believe this message applies to anyone looking to get into data engineering. There is certainly a need for both technical education and hands-on experience, but there is so much opportunity available for anyone willing to come to the table with an open mindset, ready to collaborate. If this sounds like you, the next level of your career in data engineering is waiting for you.