Top 10 Most Hired Data Roles and Skills


In the rapidly expanding technological world of today, when humans tend to generate a lot of data, it is quintessential that data is analyzed. Data is now the new front for business or I can say it has become fuel for businesses of the age. For an organization to work with data and information, an assortment of jobs can be acquired to gather, arrange, and examine the information.

From taking the information from crude functions right through noteworthy bits of knowledge, there can be some significant disarray in what obligations every information job has. In this story, I have compiled information for the most hired roles in the data industry and the preferred skills, roles, and responsibilities from my conversation with recruiters, peers or reading articles suggested by my Google Feed. Read on to know what they are!

1. Business analyst

A business analyst is responsible to analyze an organization or business, its processes, requirements, or systems. A business analyst’s primary role is to assess the business model or its integration with technology.

Simply put, a business analyst is responsible to convert business expectations into data analysis. If a core data team lacks domain expertise, a business analyst can bridge this gulf and help the business make data-driven decisions. S(he) guides the businesses to improve processes, products, services, or software-based on data analysis.

Preferred skills

Data visualization, business intelligence, SQL

Role and Responsibilities

  1. Improve business process
  2. Serve as an intermediary between business and IT requirements
  3. Data visualization tools — Tableau, Power BI, Looker, Alteryx
  4. Business Intelligence Understanding
  5. Data Modelling
  6. Conscious listening and storytelling

2. Data Analyst

According to me, a quintessential data analyst best discovers how data can be used to answer questions and solve problems.

The data analyst role revolves around the use of proper data collection and interpretation. A data analyst’s job is to make sure that the collected data is relevant and exhaustive while also interpreting the analytics results. Businesses hire data analysts to have visualization and storytelling skills, to convert isolated numbers into tangible insights.

Preferred skills

R, Python, JavaScript, C/C++, SQL

Role and Responsibilities

  1. Collect, process, perform statistical analysis
  2. Communication of visualizations
  3. Math, Stats, and Machine Learning
  4. Excel, spreadsheets
  5. Database Systems: SQL and NoSQL

3. Data Architect

“You can’t build a great building on a weak foundation. You must have a solid foundation if you’re going to have a strong superstructure.” ~ Gordon B. Hinckley

Data architecture is a structure of models, policies, rules, or standards that governs the data which is collected, and how it is stored, arranged, integrated, and put to use in data systems and organizations.

In that case, it is a data architect’s role to include designing, creating, deploying, and managing an organization’s data architecture.

A data architect is a key to a business because of their role of working with large amounts of data — yes, Big Data! Data architects closely work with cloud platforms, data warehouses, database architecture, data centralization, and ensure integrity across different sources.

Preferred skills

SQL, NoSQL, XML, Hive, Pig, Hadoop, Spark

Role and Responsibilities

  1. Create a blueprint for data management
  2. Integrate, centralize, protect and maintain data sources
  3. Data warehousing
  4. Data modeling
  5. Systems development
  6. In-depth knowledge of database architecture
  7. Develop data pipelines
  8. Extraction, Transformation, Load, BI tools

4. Database Administrator

The job profile of a database administrator is pretty much self-explanatory- they are responsible for the proper functioning of all the databases of an enterprise and grant or revoke its services to the employees of the company depending on their requirements. They are also responsible for database backups and recoveries.

How to Become a Database Administrator?
Some of the essential skills and talents of a database administrator include database backup and recovery, data security, data modeling, and design, etc. For a database administrator, being good at disaster management is certainly a bonus.

Preferred Skills

SQL, Python, Java, Ruby on Rails, XML, C#

Role and Responsibilities

  1. The database should be available to all relevant users at all times
  2. The database remains safe and performs properly
  3. Data modeling and design
  4. Disaster Management
  5. Distributed Computing (Hadoop)
  6. ERP and business knowledge
  7. Database systems: SQL and NoSQL

5. Data Engineer

Data Engineers implement, test, and keep infrastructural additives that record a data flow and design. Realistically, the position of a data engineer may be blended in a single person with a set of capabilities that could be very close: ETL, pipelines.

Data Engineering often includes data design, build, and installation of the data systems as part of their responsibilities. Data Engineers fuel machine learning and AI analytics in the data teams including data acquisition, data transformation, data modeling, and more around data processing.

Preferred skills

SQL, NoSQL, Hive, Pig, Matlab, SAS, Python, Java, Ruby, C++, Perl

Role and Responsibilities

  1. Develop, construct, test, maintain data architects
  2. Data APIs
  3. Data modeling and ETL tools
  4. Data warehousing techniques
  5. Database systems: SQL and NoSQL

6. Data journalists

To the best of my comprehension, a data journalists’ role chiefly revolves around making sense of data output by putting it in the right context. They’re also tasked with articulating business problems and shaping analytics results in compelling stories.

Though required to have coding and statistics experience, data journalists should be able to present the idea to stakeholders and represent the data team with those unfamiliar with statistics.

Preferred skills

SQL, Python, R, Scala, Carto, D3, QGIS, Tableau

Role and Responsibilities

  1. Generate ideas for data-driven stories
  2. Conceptualize development and visualization of data
  3. Report and analyze data to describe a compelling story on an array of platforms
  4. Represent the data team and the journalism team in editorial meetings

7. Data scientist

Assuming you aren’t hunting to become a unicorn, a data scientist is responsible to solve business tasks using machine learning and data mining techniques. If this is too fuzzy, the role can be narrowed down to data preparation and cleaning with further model training and evaluation.

By extrapolating and sharing implicit data insights, data scientists assist the business to clear up vexing problems with the help of data. Combining data science with data modeling, statistics, analytics, and math skills — alongside business acumen, the facts that data scientists discover the solutions to primary questions that assist businesses to make sound data scientists.

Preferred skills

R, SAS, Python, MATLAB, SQL, NoSQL, Hive, Pig, Hadoop, Spark

Role and Responsibilities

  1. Clean and organize data
  2. Predictive Modelling
  3. Storytelling and Visualizing
  4. Math, Stats, and Machine Learning
  5. Distributed Computing

8. Data & Analytics Manager

A data analytics manager steers the direction of the data science team and makes sure the right priorities are set. This person combines strong technical skills in a diverse set of technologies with the social skills required to manage a team.

Preferred skills

SQL, Python, R, SAS, Matlab, Java, NoSQL, Pig, Hive, Hadoop

Role and Responsibilities

  1. Manage a team of analysts and data scientists
  2. Cheer data wizards, solve challenges and track progress
  3. Leadership and project management
  4. Data mining and predictive modeling
  5. Database management systems: SQL and NoSQL
  6. Interpersonal communication

9. Machine Learning Engineer

A Machine Learning Engineer looks over the task of combining software engineering and modeling skills by determining which model to use and what data should be used for each model.

Probability and statistics are also the forte of Machine Learning Engineer. A day-to-day responsibility of a Machine Learning Engineer is training, monitoring, and maintaining a model.

Preferred skills

R, Python, Scala, Julia, Java

Role and Responsibilities

  1. Designing and developing machine learning and deep learning systems
  2. Running machine learning tests and experiments
  3. Implementing appropriate ML algorithms
  4. Perform statistical analysis
  5. Train and retrain data and systems
  6. Undertaking machine learning experiments and test
  7. Developing deep learning systems based on business needs
  8. Finally implementing suitable AI/ML algorithms

10. Statistician

It all started with the historical leader of data and its insights — the statistician.

A statistician’s strong background in statistical theories and methodologies, a logical and stats-oriented mindset empowers them to harvest the data and turns it into useful information and knowledge.

Statisticians can handle all sorts of data and keep exploring what’s more to do. Thanks to their quantitative background, statisticians in 2020 can quickly master new technologies and boost their intellectual capacities. A statistician brings the “mathemagic” to the table and it’s the statistician’s insights that can radically transform businesses and decisions.

Preferred skills

R, SAS, SPSS, Perl, Matlab, Stata, Python, Pig, Hive, SQL, Spark

Role and Responsibilities

  1. Collect, analyze and interpret data
  2. Conduct qualitative and quantitative data analysis
  3. Statistical theories and methods
  4. Data mining and Machine Learning
  5. Cloud Tools: AWS, Google Cloud, Azure
  6. Distributed Computing (Hadoop)
  7. Database Systems — SQL and NoSQL

While the above careers and roles are just an interpretation of what the businesses expect while looking at different data science job postings, we do shed some light on the different data science jobs that are available in today’s market. In the meantime, don’t forget to keep your data science skills up to date.

That’s it from my end for this blog. Thank you for reading! I hope you enjoyed the article. Do let me know what data roles are you looking forward to exploring in your data journey?

Happy Data Tenting!

Disclaimer: The views expressed in this article are my own and do not represent a strict outlook.

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