Data engineer vs data scientist: What’s the difference?

Take a look at the most impactful trends affecting business today — AI, machine learning, GDPR and CCPA compliance, and advanced analytics, just to name a few — and you’ll notice they all share one common trait. They all rely on clean, reliable, well-curated data.

Given the escalating volume, velocity, and variety of data flowing into and through most organizations — and the consequences for misusing it — the demand for qualified data engineers and data scientists has skyrocketed in recent years. According to a 2019 study, both “data engineer” and “data scientist” rank among the top 10 tech jobs in the United States, based on the number of active job postings.

As the task of gathering, storing, processing, and analyzing data becomes increasingly complex, organizations will continue to seek out specialists offering the skillsets, education, and experience needed to deliver results at all levels of the data management ecosystem. Two of those specialists — the data engineer and the data scientist — are often confused, yet each plays a vital role in the organization’s mission to transform data into a strategic asset. So let’s find out: what’s a data engineer vs data scientist?

What they do

Data engineers play more of a hands-on role in managing and processing raw data than do their colleagues on the data science side. It’s up to the data engineer, for example, to ensure that the organization’s data is clean, accurate, properly formatted, and stored in an efficient way. When data engineers do their job well, data scientists and others in the organization can access data promptly, know exactly what they’re looking at, and leverage it with confidence. The data engineer is also responsible for

• Developing and maintaining data architectures
• Creating the process stack for collecting, storing, and processing data
• Building APIs for large-scale processing
• Ensuring efficient data flows between systems
• Moving data between servers or clusters
• Recommending strategies for improving data quality and reliability

The data scientist functions at a higher level than the data engineer — less hands-on, more strategic. Data scientists bridge the gap between the data (as prepared and curated by the data engineer) and the stakeholders who need data-driven insights to achieve specific business goals. After the data engineer has cleaned, formatted, and stored the data, the data scientist uses analytics tools and statistical applications to prepare it for analysis. He or she then executes the analysis and presents the finished product in the form of a story that business users can understand and leverage. Other duties of the data scientist include

• Examining data to uncover hidden patterns
• Building statistical models to support business needs, such as forecasts of future sales
• Assessing and prioritizing data points (and eliminating those that do not support business objectives)
• Turning data into action through
• Product recommendations
• Intelligent chat interfaces
• Trend prediction and analysis
• Business process optimization
• Data mining and enrichment
• Automating the process so that stakeholders can receive insights on a regular basis

Where they come from

The data engineer and the data scientist may share a background in computer science, but that’s sometimes where the similarity ends. Data engineers tend to come from programming and/or engineering backgrounds and may have studied computer engineering, while the data scientist’s background tends toward statistics, mathematics, econometrics, and/or operations research. It’s also common for a data scientist have a broader knowledge of business and operations, while the data engineer may be more deeply focused on the technical side of data management.

How they add value

While the data engineer and the data scientist function at different levels of data analysis and management, each plays an equally vital role in the organization’s mission to leverage its data as a strategic asset. Without data engineers, data scientists lack confidence in the accuracy and relevance of the data — if they can even find the data they need. Without data scientists, the fruits of the data engineer’s labor may never reach the desks of stakeholders in a format that they can understand and leverage in supporting their business goals.

As data continues to evolve as a strategic and competitive asset, the demand for highly qualified data professionals will continue to grow. Businesses need talented, experienced data engineers and data scientists to help them make data-driven decisions that can grow their customer base, help them expand to new markets, increase customer satisfaction, and improve their bottom line. By distinguishing the strategic and the technical aspects of data management — and putting in place the individuals with the skills and background necessary to succeed — data-focused companies can better understand where they are today and chart a path for where their data can take them.

Anne Lifton is a lead data scientist in charge of deployment and development of data science models using Python, Kafka, Docker, PostGreSQL, Bazel, and R for production environments.