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The roles you need on your AWS Big Data team

Briefing On the key roles required for an AWS team and defining the responsibilities of data engineers, data architects, data analysts, and data scientists, along with some hiring tips

Briefing On the key roles required for an AWS team and defining the responsibilities of data engineers, data architects, data analysts, and data scientists, along with some hiring tips.

Big Data is the buzz of the town, as its importance has skyrocketed in less than a decade across a wide range of industries, causing a rise in the search for AWS experts across the tech market.

The subject of the matter is, "Do we have to split roles and responsibilities under an AWS Big Data team, and where do we start?

"The foremost step in putting together an AWS team is to organize them according to their projects and skillsets, and then build your business plans around those parameters.

Now, let's look at the essential roles required to build an AWS team

The Core Roles of an AWS Team

1. Data Architect

The fundamental role in an AWS team is the Data Architect, who is responsible for shaping the raw data and giving sensible input for the data engineers to work on. This is achieved through a well-defined data management framework.

Skill requirements

  • Experience in data-oriented programming languages like SQL, DBase, and Visual Foxpro
  • Knowledge of Excel, SPSS, and object programming languages such as Python, Java, C, C++, and Perl.

A data architect's vision is to translate business requirements into technical requirements and design for data integration, centralization, and maintenance

According to a survey by Recruiter.com, the demand for data architects will intensify at a rate of 15.94% in the coming years.

Be watchful while filling this role for your company, as it stands to be the foundation for any AWS project.

2. Data Engineer

Once the data architects have rendered their data to the cloud, the data engineers are responsible for designing, optimizing, and maintaining the data infrastructure for data collection, management, and transformation via algorithms. They are in charge of creating pipelines that convert raw data into usable formats that can be used by data scientists and other data consumers

Skill requirements

  • Hands-on experience in data warehousing, data architecture, SQL, AWS Cloud
  • Programming languages like Java and C++ are also on their plate.
  • An advanced level of Python knowledge would be beneficial in data operations.
  • Knowledge of big data platforms like Apache Hadoop, Spark, Kafta, and visualization tools like Tableau is a plus.

Data engineers also assist the data science team by developing dataset procedures that aid in data mining, modeling, and production. As their participation is critical in improving data quality, it is entirely appropriate to refer to them as the pillars of an AWS team.

3. Data Analyst

Data analysis has become one of the most in-demand jobs as businesses rely on data insights to make critical business decisions.

Their primary responsibilities include data extraction, debugging, and reorganizing data in a readable format; performing analysis and using statistical tools to identify, analyze, and interpret patterns and trends in local, national, and global markets; and preparing reports.

Further more, they collaborate with programmers, engineers, and management heads to identify process improvement opportunities, recommend system changes, and develop data governance strategies.

Data analysts are also in charge of generating the stakeholder summary report and discussing market trends

Skill requirements

  • Knowledge of programming languages like SQL, Oracle, R, MATLAB, and Python.
  • Experience in handling reporting packages like Business Objects, programming (Javascript, XML, orETL frameworks), and databases.
  • Proficiency in statistics and statistical packages like Excel, SPSS, and SAS to be used for data set analysis.
  • Experts at queries, writing reports, and making presentations
  • Eloquent in verbal and written communication.

Data analysts are the professionals who present the data legibly and create it in a logical structure.

4. Data Scientist

Are you wondering what exactly constitutes the job of a data scientist that sounds similar to a data analyst?.

A data analyst spots the trends and patterns in data, whereas a data scientist builds predictive models and creates machine learning algorithms to produce accurate forecasts.

A data scientist will create algorithms that can spot trends, train the algorithms to predict customer behavior, and help the business get ahead of the curve

Skill requirements

  • Knowledge of statistical programming languages like R and Python and database query languages like SQL, Hive, and Pig. Familiarity with Scala, Java, or C++ is an added advantage.
  • Proficiency in statistics and excellent communication skills.
  • Good knowledge of machine learning methods like k-Nearest Neighbors, Naive Bayes, SVM, and Decision Forests.
  • Strong math skills with an analytical mind.
  • Experience with data visualization tools like matplotlib, ggplot, and Tableau.

These proactive members of an AWS team are the masterminds behind the betterment of your project.

In addition to the role briefing, here are a few points to consider when hiring AWS professionals.

  • Hire an AWS Expert with Hands-on Experience.
  • Look for candidates with good communication and interpersonal skills.
  • Consider professionals with problem-solving and decision-making abilities.
  • And finally, when it comes to hiring data analysts and data engineers, look for people who are creative thinkers and constant learners.

Reaching out to professionals who practice strategic staffing approaches is the solution to add to your recruitment manual! Cloudhero.es, with its Global Partner Network of AWS professionals, assists you in building the AWS team from the bottom up.

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