While there's some overlap, data engineers and scientists aren't interchangeable. Data engineers create and maintain structures and systems for gathering, extracting, and organizing data, while data scientists analyze that data to glean insights and answer questions. The two roles also have different responsibilities, salaries, and roles.
Read on to learn more about the differences between data engineers vs. data scientists. Along the way, we'll touch on their careers, salaries, skills, and roles. We'll also cover who is best to hire for your company.
What is data science vs. data engineering?
Data science is an interdisciplinary field that uses scientific processes, methods, systems, and algorithms to extract insights and knowledge from structured and unstructured data.
As data science professionals, data scientists concentrate on spotting new insights from the data that was extracted and organized for them by data engineers. They also:
- Conduct experiments
- Create hypotheses
- Use their knowledge of data analytics, statistics, ML, BI, and data visualization to identify patterns and predict trends
On the other hand, data engineering involves designing and creating systems for gathering, storing, and analyzing data.
Accordingly, data engineers are responsible for:
- Designing, building, testing, integrating, managing, and optimizing data from various sources
- Building and testing the architectures and infrastructures that enable data generation
- Creating and optimizing data pipelines — sets of actions that move raw data from disparate sources into a data warehouse for storage and analysis
In short, data engineers build the systems and architecture that data scientists use to gather, analyze, and organize data.
Typical data scientist and data engineer jobs
The terms "data scientist" and "data engineer" encompass a multitude of roles. Here are the hottest data scientist and data engineer careers.
Types of data scientists
The main types of data scientists include:
Machine learning scientists
As their title suggests, machine learning scientists work with ML models. Besides using ML models to extract, clean, and analyze data, they also create ML models using a mix of algorithms and data.
Also known as actuaries, actuarial scientists use mathematics and statistics to predict financial risks for organizations. Most actuaries work in industries that rely heavily on risk management, such as financial speculation and insurance.
These data scientists apply statistical models and methods to real-life problems. Specifically, they collect, analyze, and interpret data to help team leads and C-suite executives make informed decisions. Statisticians play vital roles in various industries, including healthcare, business, physical sciences, and government.
Digital analytics consultants
Digital analytics consultants gather and analyze social media and website data to help brands stand out from competitors. They're also responsible for:
- Teaching teams how to use analytics platforms
- Improving website performance
- Optimizing marketing campaigns
- Improving social media presence
- Developing email marketing strategies
Types of data engineers
As with data scientists, there are multiple types of data engineers, including:
Generalist data engineers typically work in small teams with other data science professionals, like digital analytics consultants and machine learning scientists.
If generalists are one of the few or the only data science professionals at their company, they will have to take on basic data science tasks, such as collecting, processing, and analyzing data. On the flip side, if they work at a company with many data scientists, generalist data engineers will only be responsible for building and maintaining data analysis systems.
Pipeline-centric data engineers
Often found in mid-sized companies, pipeline-centric data engineers are responsible for building, testing, maintaining, and optimizing data pipelines. They also collaborate with data scientists to interpret and use collected data. Pipeline-centric data engineers usually work in bigger teams than generalists.
Database-centric data engineers
Database-centric data engineers create, maintain, and populate analytics databases. Additional responsibilities include implementing data pipelines; creating table schemas using extract, transform, load (ETL) methods; and adjusting databases for effective analysis. They often work for large organizations and conglomerates.
Data engineers and data scientists have a similar career progression. Here are the main steps in data engineers' and data scientists' careers:
- Entry-level or junior: Entry-level or junior data engineers and scientists have limited experience. Most of them are fresh college or boot camp grads who are just starting out. They report to seniors, tech leads, and team managers, who will mentor and guide them. Their main goal is to learn new skills and gain professional experience by working on real-life projects.
- Senior: After three years, entry-level engineers and scientists will become seniors. Seniors have in-depth knowledge of programming languages, frameworks, and ML models. Seniors also train juniors and work with team leads and C-suite executives on high-level business objectives.
Many senior data engineers or scientists are content to remain as seniors, but some may transition to other roles, including:
- Chief data officer: Senior data engineers and scientists with sharp business acumen may become chief data officers. These senior executives are responsible for the governance and utilization of data across the organization. Other duties include ensuring data quality, spearheading information and data strategy, and overseeing data analytics.
- Data architect: These professionals collaborate with data engineers to create blueprints and plans for advanced data pipelines and models. Like chief data officers, data architects must have strong business acumen.
- Manager of data engineering: Senior-level engineers and scientists often transition to managers of data engineering. These managers lead and coach a team of data engineers. They also focus on growing the team by taking a proactive role in mentoring, overseeing performance, and hiring decisions.
Difference between salary, skills, and roles
Data scientists and software engineers can have vastly different salaries and roles depending on their experience levels.
Salaries and skills of data scientists
The salaries, skills, and roles of data scientists vary according to seniority.
Junior and entry-level data scientist salaries, skills, and roles
Junior or entry-level data scientists have under three years of professional experience. According to Glassdoor, the average U.S.-based junior or entry-level data scientist earns $82,344 annually.
Most companies expect junior and entry-level data scientists to have the following skills:
- Zero to three years of experience with Python and SQL
- Understanding of ML models and concepts
- An eye for detail
- Passion for helping and supporting coworkers and clients
Junior and entry-level data scientists are typically responsible for:
- Leveraging diverse data sources to spot trends
- Monitoring and analyzing the performance of ML models
- Improving the efficacy of ML models
- Writing reports about how ML models can be improved
- Communicating analytical insights to key stakeholders
Senior data scientist salaries, skills, and roles
In contrast, senior data scientists have over three years of experience. They earn an average of $142,258 per year.
Most employers expect senior data scientists to have the following skill set:
- In-depth understanding of ML principles and techniques like random forests, boosting, regularization, and neural networks
- At least three years of experience with Python and SQL
- Leadership skills
- Experience collaborating with stakeholders
- Experience with advanced natural language processing (NLP) models like Bert and Transformers
Senior data scientists are responsible for:
- Modeling complex problems, spotting insights, and identifying business opportunities through mining, visualization, algorithmic, and statistical techniques
- Leading projects
- Building effective working relationships with stakeholders
- Understanding internal and external stakeholder requirements
- Mentoring entry-level and junior scientists
- Helping team leads generate ideas
- Designing experiments, models, algorithms, and hypotheses
- Conducting advanced data analysis
Salaries and skills of data engineers
As with data scientists, data engineers' salaries, roles, and skills change with their expertise level.
Junior and entry-level data engineer salaries, skills, and roles
Like their data scientist counterparts, junior and entry-level data engineers have less than three years of real-life experience. As a result, they have lower salaries. According to Glassdoor, the average U.S.-based junior data engineer earns $70,357 annually.
Junior and entry-level data engineers should have the following skills:
- Zero to three years of experience building and optimizing ETL pipelines
- Zero to three years of experience with Python and SQL
- Advanced knowledge of Microsoft Excel, including macros, PivotTables, and formulas
- Strong organizational and project management skills
Typical junior and entry-level data engineering responsibilities include:
- Collaborating with senior data engineers to create and implement data pipelines
- Building high-quality prediction systems
- Participating in the creation, development, and evaluation of data science solutions and products for internal and external stakeholders
- Working with senior data engineers to develop and optimize ML models
- Helping data scientists transform unstructured data into business solutions
Senior data engineer salaries, skills, and roles
Senior data engineers have over three years of professional experience. According to Glassdoor, the average senior data engineer in the U.S. earns $135,961 per year.
Most senior data engineers have the following skills:
- At least three years of experience in MySQL and Python
- Over three years of experience building, testing, and optimizing data pipelines
- Advanced knowledge of databases
- Proven experience using ML libraries and frameworks
- Excellent analytical and communication skills
- Advanced knowledge of ML concepts and techniques, including random forests, decision trees, boosting, and support-vector machines (SVM)
- Strong leadership, project management, and organizational skills
- Familiarity with at least one project management or software development methodology, such as Agile or Waterfall
Most companies require senior data engineers to do the following:
- Design, build, test, and maintain systems and architectures for data analysis
- Create, manage, and optimize data warehouses and data lakes for storing data
- Use code to analyze data and build models to solve problems
- Use ML to create personalized experiences for customers
- Contribute to the creation and implementation of ML and predictive algorithms
- Mentor junior and entry-level data engineers
- Create new data analysis tools and dashboards
- Develop algorithms for transforming data into actionable insights
Who is best to hire for your company?
Is a data scientist or a data engineer best for your company? There's no one-size-fits-all answer to this question. Either could be a good choice, depending on your industry, budget, preferences, and company size.
Generally speaking, you should hire a data engineer if you:
- Don't have enough data: If your company only has small data datasets, hire a data engineer. A data engineer can build your company's data infrastructure and take on vital data scientist tasks, such as data extraction and analysis. Meanwhile, a data scientist can't create infrastructure for moving raw data into warehouses for storage and analysis. They can only analyze data and derive insights via pre-existing systems and architectures.
- Have a limited hiring budget: As covered above, data engineers can perform many data scientist tasks. As such, data engineers are your best pick if you can't afford to hire both.
On the flip side, data scientists are a better pick if you:
- Already have a team of data engineers: Data scientists are meant to expand on the data engineer role. So if you already have an established team of data engineers, you can hire data scientists to analyze your data. This will give your data engineering team more time and energy to focus on building, deploying, testing, maintaining, and optimizing data systems and architecture.
- Need specialized talent: Data engineers are usually only familiar with general data science tasks. For highly specialized tasks like social media analysis, you should hire data scientists with specific skill sets. For example, you should hire digital analytics consultants for social media and website analysis.
- Have the budget for it: Data scientists can be costly to hire. Remember, they need data engineers to do their work. This means you need to hire at least one data engineer if you want to hire data scientists.
Start hiring data scientists and data engineers with Revelo
Data scientists and engineers can both use ML, algorithms, and statistics to help you solve business problems.
However, they shouldn't be confused with each other. Data scientists can't function without engineers and are only responsible for extracting actionable insights and trends from data. Data engineers, on the other hand, can perform data science tasks, although their main goal is to build architecture and systems for storing and analyzing data.
Interested in hiring data scientists and data engineers? Reach out to Revelo today. All of our talent has been rigorously pre-vetted for their skills, English proficiency, and experience.