Big data benefits numerous people who build high-salary careers. Big data experts have the highest market demand, given the current market scenario. Forbes also declared, “Machine Learning Engineers, Data Scientists, and Big Data Engineers rank among the top emerging jobs on LinkedIn”. Let us talk about the essential things you should know before getting a job in data engineering.
A data science degree will not sustain a data engineering career. Data science is a math-oriented subject, whereas data engineers work with data warehouse engineers, data platform engineers, data infrastructure engineers, analytics engineers, data architects, and DevOps engineers especially to build data pipelines.
Key Points for a Successful Career in Data Engineering
To help professionals and freshers decide, we have explained the key points which are essential for a successful career in data engineering:
A Strong Sense of Programming
Everything is code now and a data engineering job demands a strong development skill. A data engineer must have a programming background. The critical skills are SQL, Python, R, and ETL methodologies and practices. They also need to have an interest in data, and in finding patterns in data. Big data projects are more complex than small data. Hence, you need to have the ability to build complex systems, and data pipelines to become a good data engineer.
Learning Technologies
The major duties of a data engineer are:
- Effective extraction and processing of data to maintain a smooth data pipeline.
- Serve the needs of the data scientists and data analysts, who use the system.
- Monitor the cost of moving and storing data.
A qualified data engineer’s value is to know the right tool for the job at least 10 to 30 different technologies to choose the right tool for the job in technologies, such as:
- Apache Hadoop
- Apache Spark
- Apache Hive
- Apache Beam
- Apache Cassandra
- Apache Oozie
- Apache NiFi
- Apache Flink
- Apache HBase
- Apache Impala
- Apache Kafka
- Apache Crunch
- Apache Apex
- Apache Storm
- Heron
- Hue
You can also begin with the three giants in the market: Google Cloud Platform (GCP), Microsoft Azure or Amazon Web Services (AWS). Try to learn how their service offerings can be used for building scalable data pipelines.
Data engineers also need to map out their data pipeline architectures through technical diagramming. You can choose a diagramming tool like Lucidchart and learn it thoroughly from block diagrams and flow charts to AWS architecture diagrams.
Value Experience More Than a Degree
Nothing beats real-world experience. There is always a disparity between academia and industry requirements. A textbook will not be able to teach you handling a data pipeline outage. It is not mandatory to have an advanced degree to get a job as a data engineer. Anyone with experience in operations or software systems can make a smooth transition to data engineering.
Another option is starting as an analyst to understand the real value data brings to the business. Data engineers are responsible for extracting data for data scientists and data analysts, who need in a format. The data engineer has to acquire and transform the data so that the data scientists and data analysts can derive higher insights.
Social and Communication Skills
Apart from technical skills, a sound data engineer will require qualities like:
- Detail Oriented: Data quality is essential for building data pipelines. The quality and integrity of the data moving through the pipeline will determine the quality of the rest of the work.
- Creating Simple Design: A qualified data engineer should create clean designs where the architecture is not overly done.
- Good communication skills: This job role involves interacting with people to understand the use case before designing the right thing.
- Correct Mindset: Recruiters sometimes also look for people who like puzzles or putting pieces together to make a complete picture.
Evolving with The Job
Data engineering is an evolving discipline. There is a shift to data services, which brings a change in the role of a data engineer who helps to deliver data services. With the popularity of cloud data warehouses, storage and maintenance have become an indispensable jobs of the data engineer. A good data engineer should have the love of learning: to keep up with new libraries, frameworks, and tools. She should be able to quickly learn, understand and evaluate new tools.
Conclusion
If you are considering a career as a data engineer keep in mind that:
- The job is incredibly complex, and involves quick learning of new skills and techniques.
- You should start working for a start-up so that you learn efficiently and quickly as you have to wear multiple hats there.
- Finding a great mentor is difficult in this role but if you get one, follow and observe minutely.
- If you want to grow forever, dedicate enough time and effort to keep up with the technology.
Have all the above qualities in you? Then go for a successful career in data engineering.
