Data engineers are the frontline people in the analytic world. They have many titles like ETL developers, technical architects, BI developers, and data science software engineers. No matter what they are called they do the same thing. They initially analyze all the structured, unstructured, and semi-structured data that is used by businesses researches and other agencies ‘ use.
Key responsibilities They manage data. They construct and analyze database architecture. They also determine that hoe the data is going to be collected and stored. They analyze data through pipelines that help convert raw data into useful formats. They sometimes develop a system through which the people who are not part of the data world can access essential data.
Types There are mainly three types of data engineers with somehow different responsibilities. 1st ones are generalists. They are mostly working in small companies with fewer data-centered employees so they have to manage as well as analyze the data. 2nd are pipeline-centric data engineers.
They closely work with data scientists to develop tools that can help them analyze big data and achieve their goals. 3rd are database-centric data engineers. They build the pipelines for the other engineers as well as create warehouses at a large scale for data. Which takes a lot of time and helps analysts and scientists do their jobs.
Daily responsibilities Here is a general routine that might not be exactly followed by most of the data engineers. Firstly they start with maintenance work such as checking pipeline failures and looking into logs. All the found issues are fixed and it will take a lot of time.
Then they mainly focus on an ongoing project like creating a new pipeline. If the implementation plan is ready they have nothing else to wait for. Meanwhile, all the work there will be a continuous submission of duplicate or missing data complaints. These complaints take priorities mostly but sometimes the engineer keeps doing what he does. New day new task for a data engineer.
Important and useful tools The most important and widely used data engineering tools used worldwide are Apache Spark, Azure, Apache Kafka, Python, Java, Apache Hadoop, Apache Cassandra, and ETL tools. When you look forward to furthering education in data engineering.
It mainly includes programming languages and tools that were not part of the program you were enrolled in. It is also possible that those courses were not that important at that time and you don’t have a proper education.
Payscale These engineers are paid well as they give a lot. An average yearly salary is approximately $77,000. Highly-paid data engineers can earn up to $160,000. Average mid-career data engineer earn from $89,000 to $124,000.
There are possibilities that the high earner has more certifications or higher education. Some have not completed their education up to the level. It is a vast low competition and high demanding field. More than a hundred thousand jobs regarding this field are posted on indeed.com. Most of these jobs are quick to position filling which is great news.