Data Engineering

Data engineering emerged as a specialization of software engineering in response to exploding data volumes.

Data engineers design, build, and support the data pipelines that move raw data from the point of collection through successive layers of storage and processing. Their work is critical to supporting downstream consumers such as data scientists, business intelligence analysts, and end users.

What is Data Engineering?

Any obstacle to data accessibility is a problem for a data engineer to solve. Likewise, any opportunity to improve the quality, quantity, security, or efficiency of data is something a data engineer will explore.

Data engineers design, build, and support the data pipelines that move raw data from the point of collection through successive layers of storage and processing. Their work is critical to supporting downstream consumers such as data scientists, business intelligence analysts, and end users.

Data engineering emerged as a specialization of software engineering in response to exploding data volumes. When it comes to big data, simply trying to capture and leverage wasn’t enough: someone had to first create a foundation that could support a substantial amount of data. Data engineering builds that foundation.

Combining technical skills with business acumen and keen organizational awareness, data engineers understand why data is a decision-making asset, what hurdles stand between data consumers and data, and how to overcome those hurdles starting with the structure of the data itself. They are experts in both SQL and NoSQL, and they understand how concepts like data science and data hygiene best practices affect end users and drive business outcomes.

What are a Data Engineer’s Responsibilities?

A data engineer’s responsibilities vary depending on the type of organization they work within and the data they deal with. However, some of the most common responsibilities include:

  • Developing, building, evaluating, and maintaining data architectures.
  • Aligning data architectures to suit business requirements.
  • Creating processes around data sets.
  • Employing programming languages and tools.
  • Improving data quality, reliability, and efficiency.
  • Utilizing analytics programs, statistical methods, and machine learning models.
  • Preparing data before it undergoes predictive or prescriptive modeling.
  • Finding trends, patterns, and anomalies in data.

What Skill Sets do Data Engineers Need?

Data engineers require both technical and soft skills to excel in their roles.

Hard Skills Soft Skills
  • SQL
  • NoSQL
  • Python
  • Amazon Web Services
  • Kafka
  • Hadoop
  • Critical thinking
  • Business savvy
  • Strong communicators
  • Interpersonal awareness
  • Adaptability
  • Future focus

Hard Skills

Data engineering evolved from software engineering, so it makes sense that software engineering skills are important, especially as they apply to linking software with data sources. Most professionals in this field know SQL exceptionally well, but their technical skills also encompass many different programming languages and data management concepts. They will utilize all those skills to understand the ins and outs of different database types, know how to evaluate the strengths and weaknesses of each type, and manage the data structure of each.

Entry-level data engineers will largely work to keep database infrastructure up and running smoothly. Mid-level and senior engineers are more involved with planning, designing, and building that infrastructure, along with managing the work of junior data engineers. As such, data engineers must cultivate more advanced hard skills over their careers while also learning how to be effective managers.

Soft Skills

In addition to extensive technical skills, data engineers need to have exceptional critical thinking skills to solve complicated, unprecedented problems. They should also be business savvy, understanding how consumers want and need data to meet business objectives, then engineering the data to meet those objectives.

Arguably most important, data engineers are forward-thinking. They have the skills to see how technology, regulations, and business requirements will change over time, and the drive to prepare themselves and their organizations early. Data changes constantly. Data engineering concepts that work today may not work tomorrow. In the ever-evolving world of data, it’s the data engineer’s job to keep a company’s data infrastructure on par with the times. Otherwise, inaccurate or inaccessible data could become a serious competitive disadvantage.

Why is Data Engineering Important?

Without data engineering, valuable data would not be integrated, organized, secured, or accessible enough for downstream stakeholders to get any value from it. Data meshes, data products, and the data-driven insights that they deliver would either be impossible to construct, or they would not work as intended.

The consequences of poor data engineering take many forms and cut deep into a company. Failing to capitalize on data can cause companies to stumble into preventable problems, overlook prime opportunities, and miss out on innovation possibilities — putting them at a competitive disadvantage. Furthermore, data engineers can cause companies to expose data to hackers, which can be expensive and damage an organization’s reputation. Data is both the most important and the riskiest resource today’s companies have, and data engineering maximizes the value of data while minimizing its liability.

What are the Challenges of Data Engineering?

There are unique challenges of data engineering for both the data engineers themselves and the companies that employ them.

For companies

Data engineers are in high demand and short supply, making it difficult to recruit a data engineer, expensive to employ one, and hard to retain top talent. Many companies struggle to build adequate data engineering teams, leaving data consumers underserved as a result. Even when they have a full staff, rapid evolution in this field may lead to sudden and unexpected skills gaps.

For data engineers

The increasing amount of data being collected, stored, and utilized makes it difficult for data engineers to keep up with the pace (especially on understaffed teams). Compounding that problem is the need to always be learning new skills, experimenting with new concepts, or making new improvements to keep the data infrastructure from falling behind and becoming a liability. Solving challenges for data engineers unleashes opportunities for the whole company.

Related reading: 6 Data engineering challenges | How data engineering fails

How Does Data Engineering Differ From Data Science?

Data engineers and data scientists have similar objectives — facilitating access to data — but different responsibilities.

Data engineers are responsible for building, testing, and maintaining data architectures and the data pipelines that snake through them. They build the infrastructure that moves the data closer to the end consumer.

Data scientists, on the other hand, cleanse or analyze data for insights. They use things like data modeling to explore relationships in data and generate insights through experimentation. Unlike data engineers who prepare data for other’s consumption, data scientists are themselves the end users.

Smaller companies may have one person or team serving both roles — however, data engineering and data science teams are usually separate, equally robust, and yet closely aligned.

Data Engineering Made Easy with Starburst

Exceptional data engineering no longer requires a world-class team or Fortune-500 budget. See how Starbust helps any company use data engineering to their advantage.

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Starburst Galaxy

Up to $500 in usage credits included

  • Query your data lake fast with Starburst's best-in-class MPP SQL query engine
  • Get up and running in less than 5 minutes
  • Easily deploy clusters in AWS, Azure and Google Cloud
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