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Career Paths for Data Professionals – Which is the best one for you?

Data Professionals

In this post, we will explore these career paths more closely and find out which one is right for you!, I gave a brief overview of the different paths available to data professionals. There are four options that you can take:

  1. Data Engineer
  2. Data Scientist
  3. Data Architect
  4. Data Developer

Career Paths for Data Professionals

Data Engineer

So what’s the role of a Data Engineer? They collect, cleanse and integrate data from various sources. This includes using scripting languages such as Python or Power Shell to automate manual processes. If you have experience with ETL tools such as Informatica or SSIS it’ll be an advantage. The engineer has knowledge on databases especially SQL Server and works closely with developers building new products/features working in tandem with back-end developers.

Data Engineer’s responsibilities

They are responsible for creating pipelines that deliver data to applications. This means they work closely with system administrators and make use of the various tools at their disposal to accomplish this task. Data Engineers can monitor these pipelines ensuring business requirements are met throughout the process. The ETL team lead by example provides training where necessary and builds reusable code to streamline processes within their teams. They also create databases that store large amounts of data. If you’re interested in learning more about the role you should check out my earlier post here.

Data Scientist

A Data Scientist uses statistical analysis to explore huge volumes of data looking for trends and insights which can be used by businesses/companies for further research or product development. They create a model that can be tested via simulation. This information can then be used to make informed decisions or enhance products/services.

They have a responsibility to create repeatable processes for data analysis and communicate insights gained from their findings. They need to have strong programming skills to conduct statistical tests as well as working knowledge of visualization tools such as Tableau Software. It’s not expected that they use non-standard techniques but they will come across these in the course of their work and it’s important that they have the ability to apply them if necessary.

Data Scientist responsibilities

As with Data Engineers, Data Scientists should automate manual tasks (when possible). They may also employ Machine Learning algorithms and make use of predictive analytics tools such as Microsoft Azure Machine Learning Studio.

Data Architects

Last but not least, Data Architects work at a high-level designing and planning infrastructures for data storage and retrieval. They have the experience necessary to develop conceptual models of data that can be used by different systems in an organization. This role is more focused on managing teams than anything else with responsibilities such as maintaining documentation, performing capacity planning and quality assurance which often results in them acting as Project Managers too. 

Data Architect’s responsibilities

They design databases so that they’re highly scalable and also implement policies that ensure the security of these databases against unauthorized access and network attacks. They design solutions that allow for both structured and unstructured data to be stored efficiently in cloud-based applications with geographic redundancy built-in.

In this post, we have explored three different roles in Data Engineering to help you decide which one is right for you. If you’re interested in these roles further reading can be found here and here. You should also check out my earlier post on why it’s a great time to get into data engineering.

FAQs:

What do data engineers do? 

Data Engineers work in tandem with system administrators automating processes. They monitor ETL pipelines ensuring business requirements are met throughout the process. They build reusable code and databases that store large amounts of data. If you’re interested in learning more about the role you should check out my earlier post here.

What is the difference between a Software Engineer, Computer Scientist and Data Engineer? 

A Software Engineer builds end-to-end solutions that solve specific problems at scale (e.g.: Uber app). A Computer Scientist conducts research/analysis on computing technologies and creates novel ways to use existing algorithms (e.g.: DeepMind’s AlphaGo). They both work closely with developers creating new products/features but tend to be more focused on one area of development. Data Engineers work in tandem with system administrators automating processes. They monitor ETL pipelines ensuring business requirements are met throughout the process. They build reusable code and databases that store large amounts of data. If you’re interested in learning more about the role you should check out my earlier post here.

What is a Data Engineer? 

A Data Engineer uses statistical analysis to explore huge volumes of data looking for trends and insights which can be used by businesses/companies for further research or product development. They create a model that can be tested via simulation. This information can then be used to make informed decisions or enhance products/services. They have a responsibility to create repeatable processes for data analysis and communicate insights gained with the rest of their company. If you’re interested in learning more about the role you should check out my earlier post here.

Conclusion:

Data Engineers are highly skilled software engineers, computer scientists and data analysts that build systems to manage huge amounts of data. They often work closely with Big Data technologies such as Apache Spark or Hadoop but may also work on high-level frameworks like Flink which allows processing real-time streaming data. Their role requires them to take a lead in the development process (e.g.: creating an algorithm).

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