Data scientists and data engineers fulfill different positions within an organization, but often work in conjunction with one another. Below we will discuss the difference between these roles; including job responsibilities, typical projects, and the technical skills needed for each.
What is a data scientist?
Data scientists analyze mass amounts of structured and unstructured data, often including big data and data mining, with the goal of extracting knowledge and insights to be used for crucial business decisions. Data scientists have the ability to understand and translate the meaning of incoming data and tell compelling stories that explain the implications of their findings to key stakeholders.
What is a data engineer?
Data engineers employ tools and programming languages to design, build, test, and maintain Application Programming Interfaces, or API’s. This process stack is then used to accumulate, store, and process large amounts of data in real time. Data scientists rely heavily on effective architecture to bring structure and format to ever-changing datasets. Data engineers are responsible for creating these systems.
Data-driven organizations highly value people with these capabilities since a well made infrastructure can provide significant competitive advantages over companies with less-than-optimal data collection.
Data scientists and data engineers can have bachelor’s degrees, master’s degrees, or PhDs in computer science. However, this requirement is beginning to be overlooked if a candidate exhibits the necessary skills for a position. Beyond a common field of study, those involved with data engineering frequently have a programming background and use languages like Python, Java, or Scala, while data scientists often pursue education or training in mathematics, statistics, economics, or physics.
Next, let’s discuss some of the critical skills that employers seek when hiring for these positions, followed by the typical projects that these roles can expect to participate in.
Skills and requirements
- Proficiency in Python, Java, R, and SQL
- Ability to organize, present, and analyze data
- Understand how to apply best practices to data mining and cleansing
- Experience in manipulating datasets and building statistical models
- Experience in big data tools like Hadoop, Hive, and Pig
- Experience with unstructured data management efficiency
- Strong collaboration skills
- Proficiency in Python, R, C/C++, Ruby, Perl, and Java
- Ability to build and design large-scale applications
- In-depth knowledge of database solutions, especially SQL (Cassandra and Bigtable are also beneficial)
- Database architecture, data warehousing, data modeling, and data mining experience
- Capable of distributed computing and pipelining algorithms to yield predictive accuracy
- Understanding of Hadoop-based analytics (e.g. HBase, Hive, Pig, and MapReduce)
- Vast knowledge of operating systems, particularly UNIX, Linux, and Solaris
- Experience with ETL (Extract, Transform, Load) tools such as StitchData or Segment
- Note: Although machine learning is traditionally used by data scientists, having an understanding of ML can also be helpful to data engineers when constructing useful solutions for analysts.
Typical projects for each role
- Prototype ideas and create custom statistical models/algorithms (includes research and testing)
- Utilize clean data for the analysis, testing, creation, and presentation of results
- Understand company needs to better help in strategic planning and development of products/solutions
- Present results to internal stakeholders and external clients in a compelling way
- Collaborate (when needed) with data engineers to create AI/ML models
- Work closely with your team to communicate analyzes in an easily understood manner
- Design, build, test, and maintain big data infrastructures and processing systems
- Create and maintain optimal data pipeline architecture
- Build analytics tools that utilize data pipelines to deliver actionable insights
- Gather and clean raw data to prepare for analysis
- Automate manual processes and optimize data delivery
- Assemble complex datasets to fulfill functional and non-functional company needs
Demand for data practitioners
Demand for data scientists far exceeds supply in the current state of the industry. The increase of use cases and proven results from machine learning applications has caused a hiring fervor as companies look to their data to provide insights into customer behavior and cost reduction opportunities.
Additionally, data engineers were reported to have the leading number of job postings in the tech field, with an 88 percent increase during 2018.
Demand is high for both of these positions, creating opportunities for those with the necessary skills and commitment to innovation in computer science. Companies are always looking for people who can make an impact in how their business collects, analyzes, and implements data-centric insights.
Can data engineers become data scientists?
Data engineers can become data scientists, but the transition may be challenging. Though the technical skills needed to be a data scientist may be covered by a data engineer’s experience, the non-technical skills, like knowing how to analyze data and extract valuable information from it, might need refinement. One benefit of having experience in both fields is that collaboration between the two positions could be made easier, leading to more efficient architecture and analysis.
Algorithmia understands data scientists and data engineers
Algorithmia understands needs and challenges of both roles, which is why we created a serverless microservices architecture that allows organizations to deploy and govern their machine learning models at scale with ease.
See how Algorithmia helps data scientists and data engineers build better software for your organization in our video demo.