Data Engineering vs. Data Science
On the surface, the role of a data engineer and data scientist can look very similar—both are crucial to moving an organization’s data strategy forward. And both career paths offer exceptional opportunities. Let’s explore the roles and responsibilities of today’s data engineer and compare them to those of the data scientist.
Data Engineering and Data Science: Similarities and Differences
Data engineers and data scientists share a common goal: to help organizations make better use of data. Each plays an important part in helping business leaders spot new opportunities for growth and overcome complex challenges. But data engineering and data science are two distinct skill sets.
In general, data engineers are concerned with constructing, optimizing, and maintaining data pipelines and its infrastructure. Data scientists leverage the results and outputs of these data pipelines to access data and make meaning out of the data with statistics or advanced machine learning algorithms. Data scientists build complex models for analyzing data and communicate the results of their analysis to business leaders. In the past, data scientists were expected to handle many responsibilities that now fall in the data engineer’s domain. And they still fill this role in smaller organizations. But the field of big data has matured to the point where data engineering and data science are now distinct career paths.
A Data Engineer’s Roles and Responsibilities
Although data engineers perform numerous tasks, their core job responsibilities fall into three main categories. Let’s take a look at each.
Create and maintain data models and infrastructure
Data models are developed as a way to visually represent data elements and the connections between them, as well as to map out how data should be organized to meet specific business objectives. They provide consistency in the way data resources are defined and managed. Data engineers are often tasked with creating, maintaining, and at times modifying data models to meet the needs of their organization. In addition, data engineers create the mechanisms required to gather, house, generate, and clean the raw data that data scientists need to complete their work.
Building data pipelines
The data required for a specific project is often located in multiple systems and applications. Data pipelines are used to transport the data from the different source systems into a single data storage solution, most often a cloud data warehouse, for reporting, analytics, and data science. Data engineers create and maintain data pipelines, ensuring the data gets into the hands of data scientists in the format that’s easiest for them to work with.
Prepare data for analysis
Data engineers also prepare data for analysis, cleansing data that’s incomplete, corrupted, or improperly formatted. Part of the data preparation process happens in the extract, transfer, load (ETL) process, where the data is transformed and optimized before being loaded into a cloud data warehouse for storage and enrichment with other data sets.
A Data Scientist’s Roles and Responsibilities
Data scientists are business-focused analytics experts who put data to work, using their in-depth knowledge of statistics, data analytics, data visualization, and machine learning to solve specific complex business problems. They’re experts at developing hypotheses, testing their assumptions, and delivering their findings to organizational leadership in a concise, easy-to-understand format.
Identifying relevant data
Data scientists identify all data relevant to the task at hand. They often collaborate with domain experts such as sales, marketing, and operations to find the appropriate data sources to provide answers or predictions for specific business goals. Data scientists may also source additional data from business partners and third-party sources to supplement in-house data to provide models with relevant data that improves model predictive accuracy.
Train machine learning models to identify hidden patterns within the data
Data scientists generate additional representations of data through feature engineering to train machine learning models. Today, many data scientists are focused on creating machine learning models to uncover complex relationships within extremely large and diverse data sets. A common example is predictive modeling. This type of data modeling predicts the likelihood of a specific outcome based on current and historical data.
Communicate findings to stakeholders
Data scientists are tasked with conveying their findings to key stakeholders. These often include business leaders who may not always be familiar with data science terminology and concepts. Data scientists create dashboards and data applications to communicate the results of their research and analysis, making clear connections between their findings and the solutions they point to.
Sharpen Your Saw with Snowflake Certifications and Live Trainings
No matter what role you play in the process of converting raw data into actionable insights, Snowflake’s live training is an excellent way to enhance your existing skill set with cutting-edge instruction designed to help you get the most out of Snowflake. Instructor-led virtual training provides hands-on experience.
For data engineers and data scientists looking to set themselves apart as Snowflake experts, Snowflake certifications offer two credential tracks, the SnowPro Core Certification and SnowPro Advanced Certification. The SnowPro Core Certification demonstrates proficiency in applying core expertise implementing and migrating to Snowflake. The SnowPro Advanced Certification is designed for those with industry-forged experience working in Snowflake. This advanced certification series consists of five role-based credentials including stand-alone certifications for numerous roles including architect, administrator, data engineer, data scientist, and data analyst (available in late 2022). As Snowflake continues to expand the role that it plays in advanced data engineering, demonstrating competence using this technology has become a strategic asset, providing a valuable way to advance your career, set yourself apart from peers, and increase your appeal to potential employers.