Data Engineering vs. Data Science: Key Differences 📊
- Engineering, Dev & IT
When it comes to understanding data engineers versus data scientists, here's what you need to know.
Whether you’re running a budding brand or studying the secrets of the universe, research is a key factor in determining success. That’s where data engineering and data science come in handy. Both play crucial roles in the data industry, but they perform distinct roles using a diverse set of skills.
It’s important for data-focused Independents and clients seeking guidance on their business objectives to understand the distinctions between these roles. Here, we'll explore the differences between data engineering and data science, what each job entails, and how to determine which one might be the right fit for you.
What is data engineering? 🖥️💽
Data engineering is all about building and maintaining the technology infrastructure that supports the storage, processing, and analysis of large datasets. Think of it as the backbone of the data world.
Here are some of the key responsibilities of a data engineer:
- Designing and building data pipelines: Data engineering involves figuring out the best way to move data from one place to another and then building the infrastructure to make it happen.
- Optimizing database performance: Once data is safely stored in a database, it needs to be organized and optimized so that it can be accessed quickly and efficiently.
- Implementing data security measures: Data breaches can be costly and damaging: ensure that data is protected from unauthorized access is essential — especially in data-sensitive industries such as finance, healthcare, and e-commerce.
- Ensuring data quality: Maintain accuracy and reliability by identifying and correcting errors.
- Collaborating with data scientists and analysts: Work in collaboration to design and build data models and provide technical support when needed.
What is data science? 📈🔬
Data science involves the use of statistical and computational methods to extract insights and knowledge from data. The main responsibilities of a data scientist might include:
- Identifying business questions: Collaborate with colleagues and stakeholders to determine and understand key questions and goals.
- Collecting and cleaning data: Data is messy and complex, so put some trust in your counterparts — data engineers — to ensure data is accurate and ready for analysis.
- Analyzing data: Use statistical and computational methods to identify patterns and trends.
- Building predictive models: Forecast future events by considering historical data and relevant factors.
- Communicating findings: Share your insights in a clear, actionable manner through visualizations, reports, or presentations.
Data engineering vs. data science: Key differences 👁️
While data engineering and data science are closely linked, there are some key differences between their roles. Here are the main ones:
Data engineering focuses on the technical aspects of managing and optimizing data infrastructure, while data science centers on analyzing and interpreting data for insights and decision-making. Each role contributes uniquely to the data lifecycle, making both crucial for successful data projects.
Educational background and expertise
Data scientists require specialized education, typically holding degrees in statistics, mathematics, or related fields, and additional training in machine learning or data mining. In contrast, data engineers pursue a data engineer degree in computer science, information technology, or related fields, with certifications in database design or cloud computing.
Data engineer vs. data scientist salary 💸
According to Glassdoor, freelance data engineers generally earn between $80,000 and $140,000 annually, while data scientists bring in between $100,000 and $170,000 per year. Keep in mind that these rates can vary significantly depending on location, experience, and the rates you set for your services.
Skills and tools 🧰
Both data engineer and data science roles involve working with data, but their skill sets differ. Here are some skills and tools for each role to master:
Skills and tools for data engineers 📐
- Programming skills: Data engineers need to be proficient in at least one programming language, such as Python or Java, in order to build and maintain their data pipelines.
- Database design: They need to understand how to design and implement efficient and scalable databases.
- Data warehousing: Data engineers must understand how to design and build data warehouses used to store and analyze large datasets.
- Popular tools used by data engineers include: Apache Hadoop, Apache Spark, and SQL databases, which are used for big data processing, distributed computing, and managing relational databases.
Skills and tools for data scientists 🔬
- Statistics and machine learning: Data scientists need strong foundational knowledge in statistics to analyze and interpret data effectively and in machine learning to build predictive models that identify patterns in their data.
- Data visualization: They need to be able to communicate their findings and create compelling visualizations for stakeholders who may not have the same technological know-how.
- Data wrangling: Data scientists need to be proficient in cleaning, manipulating, and transforming data to prepare it for analysis.
- Popular tools used by data scientists include: Programming languages such as Python and R, which are used for data analysis and modeling, as well as data visualization tools such as Tableau.
Choosing your data career: What’s right for you? ✅
When it comes to pursuing a career in data, it's important to consider your personal interests and strengths in the field to determine which path is right for you. Here are some things to keep in mind:
Data engineers tend to:
- Enjoy discovering new ways to make software and systems more efficient
- Be interested in designing and building data infrastructure to support business needs
- Be excellent at programming languages such as Python or Java
Data scientists tend to:
- Be analytical thinkers who are constantly curious and enjoy testing hypotheses
- Be interested in analyzing data to find insights and make data-driven decisions
- Have a strong foundation in statistics and machine learning
- Are proficient in programming languages such as Python or R
Looking to hire a data engineer or data scientist? 🤩
Whether you’re on the lookout for a data engineer or data scientist (or both!), here are some considerations to keep in mind to ensure you're bringing on the right talent for your project:
- If you need help building and maintaining data pipelines, optimizing database performance, or implementing data security measures, a data engineer may be the best fit.
- If you need help building predictive models, conducting A/B testing, or identifying insights from large datasets, a data scientist may be the best fit.
- Both data engineers and data scientists are necessary to ensure the project is a success, and they must work well together. Keep that in mind if you’re trying to match a new hire with an existing colleague!
- Consider your project’s goals and available resources. If your budget allows just one new hire, think carefully about which role will be best suited to your needs.
Key takeaways for success 🏆🔑
Knowing the differences between data engineering and data science is crucial for anyone pursuing a career in the data industry or hiring professionals for their projects. Although both roles are essential for the data lifecycle, they require different skill sets and focus areas. By considering your interests, strengths, and project goals, you can determine which path is right for you and your project.
If you’re an independent data scientist or engineer looking to ply your trade, Contra is the place to do it. Our commission-free platform will help you find remote data science or data engineer jobs and connect with industry experts. Or maybe you’re a client in need of data-related services? Contra also provides a platform for finding top-rated and qualified Independents to help you achieve your data-related projects. With the right understanding of the differences between data engineering and data science, you can make informed decisions and set yourself up for great success in the data industry. Head to Contra.com to get started today.