Data Engineer Profession

Data Engineer Profession

Who is an Engineer?

An engineer is someone who is trying to come up with a solution to a new and fixed problem. As it is easy for anyone to find a solution but you get the best solution from a set of solutions. If you can't get, then you can't be an engineer. They design, build, monitor, launch, drive, move forward, speed up the world, gears of progress, the future of the nation.
Data science engineering is a branch of engineering where the engineers build and maintain software infrastructure that integrates large data sets. Information engineers are just software engineers. Software such as Hadoop, Spark or Redshift with information on distributed applications.
For some companies, their job may include information for Maths and Mathematics.

What are the career paths that a data engineer can take?

There are many career paths taken by data engineers. Here is a job ladder for a major technology company:

• ·       Small Google data engineer
• ·       Google data engineer
• ·       Senior Google data engineer
• ·       Get a data engineer
• ·       Head of Data science engineering
• ·       Information Officer

No doubt data science engineering is the most demanding skill in the market for now and the future of data science engineering is going to be aggressive as we are leaving millions of digital footprints every day. You need to keep enhancing your skills in order to sustain career growth. You have to be better knowledgeable in the field of machine learning and deep learning. You have good opportunities to take care in this field because India needs more google data engineers who can imitate his brain.

If you have ever been interested in doing data science engineering work, you have amazing opportunities waiting for you.

There are many career options in data science engineering.

Data science engineering jobs include:

• ·       Google data engineer
• ·       Data builder
• ·       Large data builder
• ·       Handloom Engineer
• ·       BI Engineer
• ·       Data platform engineer
• ·       Software Developer
• ·       Big Google data engineers

Is Data Engineer a Good Profession?

How can I start a career in data science engineering?

Start with the basics of data pipelines & enhance your CS basics and software engineering knowledge; it will help you start your baby step in the data science engineering domain.

Here are the steps mentioned according to which you need to learn in Order to become a google data engineer:

·       Learn a programming language efficiently

·       Get hands on scripting & automation

·       Understand how to model data

·       Command on data processing techniques

·       Learn about cloud computing

·       Learn about infrastructure internalization

I will also suggest that once you have hands-on learning experience of these tools, try to build a project in the same domain which will help you to implement what you have learnt.

So that’s another way to get your knowledge checked by yourself & see where it can be improved if needed.

What does a Google Data Engineer Do?

Google data engineers work quietly in secret to make the calculations happen.

We usually need our data engineers to know more about a few basic technologies:

Data platforms - whether related data, NoSQL, Hadoop, or Spark - depending on the need the engineer needs to be able to provide compliance and access protocol.

ETL - workflow tools or program libraries to extract data, move data back and forth and convert it for inclusion on data platforms.

Connectors - perhaps the most important toolkit - engineers should know the various ways to connect to programs together to build pipelines - HTTP, REST, SOAP, ODBC, FTP to name a few. Information is everywhere, and it is the job of the engineer to make the movement work smoothly.

Engineers in any team have four main areas to deal with:

Data Entry – to extract data from sources. Consider different data formats and processes; the goal is to extract quickly and accurately without errors.

Data storage - save and enter information extracted from the format or storage for future use. Clean and manage incoming data. If necessary, make data modeling for data for easy analysis.

Enable analytics - display data in analytics applications - form and correction depending on user: business intelligence, forecasting predictions, or machine learning.

Data Applications - to improve or assist the performance of productive analysis - whether simple spread reports, simple webforms, web or mobile applications.