[Infographic] The Anatomy of a Data Team — Different Data Roles

Data Science and machine learning have never been more popular. As demand for data roles grows, the possibilities are endless when it comes to pursuing a career in data. This infographic presents a high-level overview of the different data roles within a data team, what are the different skills required to succeed, upskilling paths to get there, and more.

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Data Consumers

Data consumers use data to make data-driven decisions and actively have informed conversations with data practitioners. 

Most commonly used tools

• Business Intelligence Tools like Tableau or Power BI
• Spreadsheets tools like Excel or Google Sheets

Possible job titles

Data consumers can have a variety of job titles. Anyone consuming data-driven insights falls under the data consumer category. Possible job titles include:

• Chief Marketing Officer
• Human Resources Manager
• Head of Sales and Business Development

Skill levels

Beginner Skill Level

• Understands what data scientists, machine learning scientists, and data engineers do
• Knows which questions can (and can't) be answered with data 
• Interpret the results of data projects, including calculations and visualizations.

Intermediate Skill Level

• Is able to calculate descriptive statistics
• Can draw common data visualizations
• Understands the business applications of data

Advanced Skill Level

• Has a strong grasp of the fundamentals of business intelligence

Upskilling path

Skill Tracks

• Understanding Data Topics
• Data Skills for Business

Courses

• Introduction to Power BI
• Introduction to Tableau

Business Analysts

Business Analysts are responsible for tying data insights to actionable results that increase profitability or efficiency. They have deep knowledge of the business domain and often use SQL alongside non-coding tools to communicate insights derived from data. 

Most commonly used tools

• Business Intelligence Tools like Tableau or Power BI
• Spreadsheets tools like Excel or Google Sheets
• SQL Databases

Possible job titles

• Business Analyst
• Marketing Analyst
• Data Analyst
• Supply Chain Analyst

Skill levels

Beginner Skill Level

• Is able to calculate descriptive statistics. 
• Can draw common data visualizations. 
• Understands the business applications of data.

Intermediate Skill Level

• Has deep knowledge of the business domain. 
• Is able to report and communicate using data.

Advanced Skill Level

• Can create dashboards. 
• Organizes data to solve business questions.

Salary Range

• According to Glassdoor, the average yearly US Salary for Business Analysts is $77K and can range from $55K to $108K

Upskilling path

Skill Tracks

• Intermediate Spreadsheets
• Tableau Fundamentals
• SQL for Business Analysts

Career Tracks

• Data Analyst with Power BI Career Track

Courses

• Analyzing Business Data with SQL

Data Analysts

Similar to Business Analysts, Data Analysts are responsible for analyzing data and reporting insights from their analysis. They have a deep understanding of the data analysis workflow and report their insights through a combination of coding and non-coding tools.

Most commonly used tools

• Programming tools like R or Python
• Business Intelligence Tools like Tableau or Power BI
• Spreadsheets tools like Excel or Google Sheets
• SQL Databases

Possible job titles

• Business Analyst
• Marketing Analyst
• Data Analyst
• Supply Chain Analyst

Skill levels

Beginner Skill Level

• Is able to calculate descriptive statistics. 
• Can draw common data visualizations. 
• Understands the business applications of data.

Intermediate Skill Level

• Perform the data analysis workflows, including importing, manipulating, cleaning, calculating, and reporting on business data
• Has a strong grasp of business intelligence tools

Advanced Skill Level

• Can create dashboards 
• Organizes data to solve business questions

Salary Range

• According to Glassdoor, the average yearly US Salary for Data Analysts is $69K and can range from $46K to $106K

Upskilling path

Skill Tracks

• Tableau Fundamentals

Career Tracks

• Data Analyst with Power BI Career Track
• Data Analyst with Python Career Track
• Data Analyst with R Career Track
• Data Analyst with SQL

Data Scientist

Data Scientists investigate, extract, and report meaningful insights in the organization’s data. They communicate these insights to nontechnical stakeholders and have a good understanding of machine learning workflows and how to tie them back to business applications. They work almost exclusively with coding tools, conduct analysis, and often work with big data tools

Most commonly used tools

• Programming tools like R or Python
• Command Line tools like Git or Bash
• Big data tools like Airflow or Spark
• SQL Databases

Possible job titles

• Data Scientist 
• Analytics Engineer
• Data Analyst
• Marketing Data Scientist

Skill levels

Beginner Skill Level

• Perform the data analysis workflows, including importing, manipulating, cleaning, calculating, and reporting on business data
• Understands the business applications of data

Intermediate Skill Level

• Understands fundamental statistics, including distributions, modeling, and inference
• Designing simple experiments such as A/B tests
• Can create dashboards

Advanced Skill Level

• Applies analysis to business applications such as finance, marketing, and healthcare 
• Understands supervised and unsupervised machine learning workflows
• Work with non-standard data types, such as time series, text, geospatial, and images.

Salary Range

• According to Glassdoor, the average yearly US Salary for Data Scientists is $117K and can range from $82K to $167K

Upskilling path

Career Tracks

• Data Scientist with Python Career Track
• Data Scientist with R Career Track

Machine Learning Scientist

Machine Learning Scientists design and deploy machine learning systems that make predictions from the organization’s data. They solve problems like predicting customer churn and lifetime value and are responsible for deploying models for the organization to use. They work exclusively with coding-based tools.

Most commonly used tools

• Programming tools like R or Python
• Command Line tools like Git or Bash
• Big data tools like Airflow or Spark
• SQL Databases

Possible job titles

• Data Scientist 
• Research Scientist
• Machine Learning Egineer 

Skill levels

Beginner Skill Level

• Perform the data analysis workflows, including importing, manipulating, cleaning, calculating, and reporting on business data

Intermediate Skill Level

• Performing supervised and unsupervised machine learning workflows including feature engineering, training models, testing goodness of fit, making predictions
• Applies analysis to business applications such as finance, marketing, and healthcare

Advanced Skill Level

• Perform deep learning workflows
• Work with non-standard data types, such as time series, text, geospatial, and images
• Deploy machine learning models in production

Salary Range

• According to Glassdoor, the average yearly US Salary for Machine Learning Scientists is $137K and can range from $97K to $194K

Upskilling path

Skill Tracks

• Deep Learning with NLP Track
• Image Processing in Python

Career Tracks

• Machine Learning Scientist with Python
• Machine Learning Scientist with R

Statistician

Similar to Data Scientists, Statisticians work on highly rigorous analysis, which involves designing and maintaining experiments such as A/B tests and hypothesis testing. They focus on quantifying uncertainty and presenting findings that require exceptional degrees of rigor, like in finance or healthcare

Most commonly used tools

• Programming tools like R or Python
• Command Line tools like Git or Bash
• SQL Databases

Possible job titles

• Data Scientist 
• Inference Data Scientist 
• Clinical Data Analyst

Skill levels

Beginner Skill Level

• Perform the data analysis workflows, including importing, manipulating, cleaning, calculating, and reporting on business data
• Understands the business applications of data

Intermediate Skill Level

• Perform statistical modeling workflows, including feature engineering, training models, testing goodness of fit, and inferring significance
• Test hypotheses and design simple experiments such as A/B tests

Advanced Skill Level

• Design more complex experiments and understand Bayesian statistics
• Understand specialist models, such as survival models, generalized additive models, mixture models, structural equation models

Salary Range

• According to Glassdoor, the average yearly US Salary for Statisticians is $88K and can range from $61K to $131K

Upskilling path

Skill Tracks

• Applied Finance in Python
• Statistics Fundamentals with R

Career Tracks

• Statistician with R
• Quantitative Analyst with R

Programmers

Programmers are highly technical individuals that work on data teams and work on automating repetitive tasks when accessing and working with an organization’s data. They bridge the gap between traditional software engineering and data science and have a thorough understanding of deploying and sharing code at scale.

Most commonly used tools

• Programming tools like R or Python
• Command Line tools like Git or Bash
• SQL Databases

Possible job titles

• Software Engineer
• Data Scientist
• Dev-Ops Engineer

Skill levels

Beginner Skill Level

• Write functions to avoid repetitive code
• Benchmark and optimize code to improve performance

Intermediate Skill Level

• Develop best practices for testing code
• Work with web APIs
• Develop packages for sharing code

Advanced Skill Level

• Develop data pipelines and work with parallel programming
• Understanding programming paradigms, such as functional programming and object-oriented programming

Salary range

• According to Glassdoor, the average yearly US Salary for Programmers (Software Engineers) is $108K and can range from $73K to $160K

Upskilling path

Courses

• Unit testing for data science in Python

Career Tracks

• Python Programmer
• R Programmer

Data Engineers

Data Engineers are responsible for getting the right data into the hands of the right people. They create and maintain the infrastructure and data pipelines that take terabytes of raw data coming from different sources into one centralized location with clean, relevant data for the organization.

Most commonly used tools

• Programming tools like R or Python
• Command Line tools like Git or Bash
• SQL Databases
• Big data tools like Airflow or Spark
• Cloud platforms like AWS, GCP, Azure

Possible job titles

• Data Engineer
• Software Engineer
• Dev-Ops Engineer

Skill levels

Beginner Skill Level

• Efficiently extract, transform, and load data

Intermediate Skill Level

• Process data and automate data flows using the command line
• Process data in the cloud
• Manage, optimize and process big datasets and large databases

Advanced Skill Level

• Develop data pipelines and work with parallel programming
• Understanding programming paradigms, such as functional programming and object-oriented programming

Salary range

• According to Glassdoor, the average yearly US Salary for Data Engineers  is $112K and can range from $76K to $166K

Upskilling path

Career Tracks

• Data Engineer with Python

Skill Tracks

• SQL Server for Database Administrators