Data science is flourishing with many different opportunities and positions. Choosing the right career path first requires determining which position most interests you. Initially, these positions may appear really similar. You might see significant overlap in the job description of a data engineer and a data scientist, but there are key differences. Each of these roles needs unique expertise to identify catalysts for growth and enhance business processes.
But what if you have the skills of both a data engineer and a data scientist? Is there one career path that involves both these areas of expertise?
1. Understanding the fundamentals of data engineering and data science
What is a data engineer?
Data engineers are curious, skilled problem-solvers who are able to read data and build solutions that enable others to make impactful decisions. Their main task is to build infrastructure and architecture to generate data. Data engineers assist data scientists and analysts in providing the infrastructure and tools needed to help businesses deliver end-to-end solutions. They usually come from a software engineering background and are highly skilled in programming languages such as Java, Python and Scala.
What is a data scientist?
Data scientists use advanced mathematics and statistics to analyse data. They constantly engage with data infrastructure built and maintained by data engineers. They are mainly responsible for conducting advanced market and business operations research with sophisticated machines and tools to identify trends and patterns in big data sets.
2. What are the main differences between a data engineer and a data scientist?
A data engineer and a data scientist have differing requirements, roles and responsibilities.
Data engineers are usually involved in developing, constructing, testing and maintaining database architectures and large-scale processing systems. To be hired as a data engineer, employers look for candidates with many technical skills that will help solve complex problems.
Data engineers are also involved in cleaning and wrangling raw unstructured data for analysis. This also calls for excellent communication skills to communicate to non-technical teams.
A data engineer needs to have the following skills:
- Good knowledge of programming languages such as Python, Java, C++, Scala.
- Advanced knowledge of SQL, including writing and debugging.
- Experience in developing and managing big data architectures and pipelines.
- Knowledge of manipulating, processing and extracting valuable insights from big and disconnected data sets.
- Automation and configuration management experience.
Data scientists typically clean and organise big data sets, use advanced analytics and create machine-learning and artificial intelligence models. They leverage their background in mathematics and statistics in combination with programming languages to identify and examine data, looking for hidden patterns in the datasets. Then they present the findings to key stakeholders.
A data scientist’s role demands the following skills:
- Prototyping ideas, researching and developing mathematical and statistical models and running experiments.
- Driving business solutions by iterating data-driven insights derived from big data sets.
- Building tools and models that help in monitoring and analysing the performance and accuracy of data.
- Improving and maintaining existing data science products.
Data scientists need data engineers or data engineering skills. Data engineers play an important role in the data value-production chain and are seldom in the spotlight, but without them, the chain breaks down.
3. Languages, tools and software used by data engineers and data scientists
A data engineer is responsible for extracting, cleaning and wrangling data, making it easy for data scientists to explore, identify patterns and build models. To do this, a data engineer needs skills in various platforms and programming languages.
- Programming languages – Python and R.
- Database knowledge – SQL, MongoDB.
- Cloud migration – AWS, Microsoft Cloud Azure, Google Cloud Platform.
- Data warehousing – PostgreSQL, Hadoop, PIG, Hive, MapReduce, Apache Spark, Kafka.
- Machine learning and AI.
The tools, languages and software used by data scientists are:
- Programming languages – Python, R or Julia.
- Visualisation tools – Tableau, Sisense, Datawrapper.
- Database knowledge – SQL, Spark.
- IDE – Anaconda, Pycharm, Atom.
- Machine learning and AI – Google Cloud AutoML, BigML.
4. Requisite data engineering skills for data science
Both data engineering and data science skill sets are necessary for data teams to perform efficiently. Data engineers and data scientists have a lot of overlapping skills, such as analysis, programming and big data. However, a data scientist’s analytics skills will be more advanced than a data engineer. On the other hand, a data engineer’s programming skills are well beyond that of a data scientist’s programming skills.
A data scientist that has the skills of a data engineer will have an edge over the others due to their ability to create a data pipeline. Here’s a list of essential data engineering skills that will help you perform well as a data scientist:
- Strong programming knowledge – Python and R.
- Solid understanding of operating systems.
- Excellent knowledge of databases – SQL and NoSQL.
- Data warehousing tools – Hadoop, HIVE, PIG, Apache Spark.
- Foundational knowledge of machine learning tools.
If you are coming from a data engineering background and want to move into data science without giving up your data engineering skills, machine learning engineering is the way to go. Machine learning engineers are professionals with combined skills in data engineering and data science. A machine learning engineer is capable of rewriting a data scientist’s code and optimising machine learning or AI codes to ensure all the codes run well.
5. Job and remuneration outlook of a machine-learning engineer
The average base salary of a machine learning engineer in Australia is around A$106,700 per year and S$86,000 per year in Singapore. Both figures are estimates from the internet employment agency Indeed and are regularly updated.
Remuneration for all data science jobs can be researched on the internet. Salaries vary significantly based on years of experience, the type of organisation offering the work – is it an academic institution, large corporation, or medium-sized company – and, in the case of Australia, where in the country it is based. Positions in Sydney, for example, tend to have higher pay.
You have to spend only a little time on the internet searching for a position to see the demand. Companies are looking for data professionals with a dynamic skill set, capable of performing multiple tasks related to big data.
If you’re an experienced data engineer looking for better opportunities in the big data industry, it’s time to upskill to data science and reap the benefits. The Institute of Data delivers full-time and part-time programs that will help you achieve your career goals and connect you with thousands of industry partners seeking professionals with skills in data science. Get job ready in 3-6 months. Talk to a career consultant now.