Who is Data Engineer?
A Data Engineer is a person, fully equipped with knowledge of hardware, databases, data processing at scale and computer engineering and who can build data infrastructure, manage data storage and use and Implement production tools. He is one of the key members of a data science team.
If you want to understand why in recent days, data engineering is getting very challenging then first thing you should know what does a data engineer do? They do things like building your data pipeline infrastructure, so basically, they build out your databases and the hardware for that. Sometimes they might involve purchasing equipment and organizing it within your organization. They build out your system for actually computing on that infrastructure, whether it’s what servers they’re going to buy and how they’re going to organize that. And what software they’re going to run on top of the database, what software they’re going to run on top of the server. They might manage the data storage and use and they might monitor how those work. They might monitor what people are using which data. They might pull data out and give it to somebody. And then they might implement production tools.
The main challenge is organization or your employer expects each of these different qualities should be equipped with one person that does all of the above things which is practically impossible. There are a couple of key characteristics that an organization looks for when they are going to hire a data engineer. And the list is really broad.
- Database architectures
- Hadoop-based technologies (e.g. MapReduce, Hive and Pig)
- SQL-based technologies (e.g. PostgreSQL and MySQL)
- NoSQL technologies (e.g. Cassandra and MongoDB)
- Data modeling tools (e.g. ERWin, Enterprise Architect and Visio)
- Python, C/C++ Java, Perl
- MatLab, SAS, R
- Data warehousing solutions
Optional Technical Skills
- Statistical analysis and modeling
- Predictive modeling, NLP and text analysis
- Machine learning
- Data mining
- UNIX, Linux, Solaris and MS Windows
A Data Engineer should know how to build the infrastructure that’s useful for organization. Ideally, they’ll be closely collaborative with the data scientists and data architects. They need to be able to work under pressure because data infrastructure for an organization is often very critical. And if it goes down then your website might go down, or you won’t be able to do any analysis, or the organization sort of grinds to a halt. Having a data engineer that’s able to work well under pressure, that’s able to keep things up and running, and keep things maintaining, that makes good decisions about software maintainability, and hardware maintainability, is somebody that’s critical for your data engineering team.
So, you understood why Data Engineer is challenging job role that every company need from Startup to large data firms. If your company is in early stage or if you build a startup for data science or analytics, then first and foremost you need to hire a Data Engineer not a Data Scientist. Because in initial stage you need to focus on infrastructure building, secure and salable data storage and data access.
The question is, what minimum qualification and skills do you need to become a Data Engineer?
The background for data engineers is often computer science and computer engineering, but they could also come from other places. They might come from a quantitative background with some computer science experience that they picked up maybe in online courses or in courses in person. Or maybe they come from information technology where they’ve actually been involved in infrastructure building and so forth.
Is a master’s required? It depends on the job you are going to apply. Some employers are more than willing to accept relevant work experience and proof of technical expertise in lieu of a higher degree.
What About Certifications?
If you’re interested in building up specific skills, you’ll find a lot of specific certifications like Oracle, Microsoft, IBM, Cloudera etc.
To be a good Data Engineer you need to have knowledge in below fields:
- Hadoop-based technologies like MapReduce, Hive, and Pig
- SQL based technologies like PostgreSQL and MySQL, Teradata, Amazon Redshift, Microsoft Data Warehouse etc.
- NoSQL technologies like Cassandra and MongoDB
- Data warehousing