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The secrets to becoming a great data engineer

Female data engineer in a data centre

ARTICLE SUMMARY

Shadi Rostami cracks open the secrets to becoming a great data engineer. Shadi is the Senior/Executive VP of Engineering at Amplitude, a leading digital analytics platform that helps companies build better products.

Data engineers are among the fastest growing roles within the UK job market.

This increase in demand isn’t surprising given the immense value—and staggering volume—of data organisations are accruing today.

While many companies have vast, continually expanding data sources, they often don’t fully comprehend the extent of their data’s value nor the critical role it plays in driving informed business decision-making. This is where data engineers come in. Landing the position requires more than just technical prowess. Role specific skills are undoubtedly crucial, but to excel as a data engineer, you must be able to understand the bigger picture of what problem you are trying to solve for your customers. Here’s how to secure the role and propel your data engineering career forward.

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Developing Data Literacy

Data engineers design, manage, and optimise data flows while building data pipelines for business analysis.

Beyond data collection and curation, data engineers empower organisations by providing self-serve data that can be accessed company-wide. They also often collaborate with data scientists and other teams, such as marketing and product, to foster data-driven decision-making throughout the organisation. Essentially, data engineers form the bridge between data insights and non-technical personnel.

The roles and responsibilities of a data engineer are extremely varied and often dependent on the size and services of the organisation. Still, there are several essential skills that will set you up for success:

  • Analytics – Understanding modern data stacks & tools used by finance and HR teams.
  • Programming – Expertise in Python, Java, and C++.
  • Databases – Knowledge of SQL & NoSQL databases, understanding of data lakes.
  • Data Modelling – Ability to accurately model data structures and relationships.
  • Data Integration – Understanding of Extract, Transform, Load (ETL) or Extract, Load, Transform (ELT).
  • Data management – Data governance expertise & pipeline management skills.

However, it’s important to understand that studying these skills will only take you so far. There are nuances to the role of the data engineer that can be learnt through work experience. As new optimisation methods emerge, these skills continually evolve, so remain open-minded and be willing to adapt as the industry progresses.

Cultivating Soft Skills

Technical skills will always be important, but soft skills such as exceptional communication, empathy, and collaboration are equally vital for becoming a well-rounded data engineer.

Closely partnering with business units to understand which insights matter and why is vital. This allows engineers to build systems that enable specific teams like product or marketing to swiftly and accurately self-serve. Effective data engineers play the role of a translator who interprets data for teams, customers, and partners. Organisations heavily rely on data comprehension across internal and external operations, which means communicating those insights clearly and effectively is critical.

Across any role, empathy is an important skill to possess, and this is no different for data engineers. Those who spend time interacting with their customers have an opportunity to not only solve complex problems faster, but also to gain a better understanding of their customer’s business goals. With that knowledge, you can proactively serve customers as a true partner rather than just a vendor.

For aspiring data engineers, collaboration should also be a key area of focus. It’s vital that data engineers aren’t working in silos but are communicating with everyone within an organisation, from product teams to data scientists. By openly communicating and sharing data insights with other teams early in the process, it ensures that everyone is aligned with the same company goals.

Adopt an Ownership Mindset 

Finally, it’s critical that every data engineer has the ability to think widely and adopt a business-first mindset rather than an engineering-first one. When making engineering decisions, it’s important you aren’t just looking through the eyes of your role but are also considering the implications this will have across the entire organisation. Before you commit to an idea, ask yourself, “If this was my company, what would I do?” Would this change your decision? The ability to think beyond your responsibilities as a data engineer is vital for career success.

One engineering manager I work with has taken this ownership mindset to a new level. They created dashboards to understand which customers may benefit from the product they’re working on and have met with some of these customers to understand their pain points better. This is above and beyond the role of a data engineer—and the mark of a true owner. 

In today’s data-driven world, companies must take advantage of the critical insights data engineers provide for informed decision-making. To excel as a data engineer, you need to have technical skills like programming, data modelling, and understanding data tools and pipelines. But you cannot skip the soft skill development. Skills like communication, empathy, collaboration, and ownership are ultimately some of the skills that will set you apart. By mastering a blend of technical prowess and interpersonal capabilities, data engineers can propel their careers forward as indispensable assets driving organisational success through data.

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