5 Roles in Data in 2021

Data Scientists, Analysts, Data Engineers, and Machine Learning Engineers. What do they do?

According to World Economic Forum, High Scalability, by 2025, the amount of data generated each day is expected to reach 463 exabytes globally. That’s a billion gigabytes! Google, Facebook, Microsoft, and Amazon store at least 1,200 petabytes of information.

To utilize this eruption of data, data professions have become ubiquitous in companies and organizations worldwide.

In this article, I will introduce you to the common roles in a data team.

Data team

A data team typically consists of:

Data Analyst
Business analyst
Data scientist
Data Engineer
Machine Learning Engineer

For each of these roles, we’ll go through

Overall description
Roles and responsibilities
Skills

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Let’s dive in!

Business analyst

Business analysts are commonly known as the intermediaries between management and the IT department in a company.

Their primary responsibility is to analyze the structure of a business, identify problems within it, and then improve the process, service, or product of a business through data analysis and software.

For example, a business analyst could conduct a market analysis and analyze the overall profitability.

Business analysts are critical when a data team lacks domain expertise, as they can bridge the gap and ensure the business is making data-driven decisions.

Roles and Responsibilities (source)

Creating a detailed business analysis, outlining problems, opportunities, and solutions for a business
Budgeting and Forecasting
Planning and monitoring
Financial modeling
Variance Analysis
Pricing
Reporting
Defining business requirements and reporting them back to stakeholders

Skills

SQL
Business Intelligence
Advanced Excel
Data visualization tools using Quicksight, Tableau, Power BI
Technical writing and strong communication
Stakeholder analysis

Data Analyst

The primary responsibility of a data analyst is to discover how to use data to answer questions and solve problems.

They work with data engineers to access data sources and with stakeholders to create relevant and meaningful reports.

Once they discover the hidden patterns in data, they will utilize reporting tools and storytelling skills to turn numbers into tangible insights.

Data analysts are crucial as they allow businesses to maximize the value of their data assets and use analytics to inform strategic business decisions.

Roles and responsibilities

Interpret data and identify trends and patterns with statistical techniques
Identify trends and patterns in data.
Prepare reports and presentations for management or clients
Effectively communicate with stakeholders to understand data and business requirements
Data mining from primary and secondary sources
Define KPI and metrics

Skills

In-depth knowledge of statistical methodologies and data analysis techniques
SQL
Programming languages such as Python or R
Spreadsheet tools — Excel
Data visualization software — Tableau, Looker, PowerBI
Cloud Technology
Strong verbal and written skills

Data scientist

The primary responsibility of a data scientist is to extract value from data using statistical techniques and machine learning.

They are jack of all trades who combine statistics, programming, data modeling, and business acumen to discover solutions to business questions.

“Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician.”

Aside from cleaning and wrangling data, data scientists spend most of their time asking questions, running experiments to answer those questions, working with stakeholders, and communicating their findings with the help of data analysts.

An example of a data scientist’s job is machine learning to increase and optimize customer experience, implement AB testing on new features, ad targeting, etc.

Roles and responsibilities

Work with stakeholders to identify opportunities for leveraging company data to drive business solutions.
Mine and analyze data from the company database to improve business strategies
Develop time series, Forecasting, anomaly detection, and user behavior, models
Define KPIs, build automated dashboards, reports, and models
Develop custom machine learning models
Implement AB testing and QA
Coordinate with ML engineers to deploy and monitor ML models

Skills

SQL
Programming — R or Python
Statistical and data mining techniques
Distributed Computing tools — MapReduce, Hadoop, Hive, Spark
Time series and Forecasting
Causal inference
AB testing
Machine Learning
Deep Learning

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Machine Learning Engineer

ML Engineers and Data scientists are pretty similar, with the differentiator being ML engineers are focused on the engineering side of machine learning services.

The primary goal of a ML engineer is to research, design, build, deploy, and test ML systems with various tools and frameworks such as PyTorch or Tensorflow for modeling, and cloud technologies such as AWS and GCP.

However, they don’t do it all alone. ML engineers partner with data scientists and engineers to find the right data, verify data quality, research and implement ML algorithms, define evaluation metrics, run tests to improve models, etc.

Roles

Work with data scientists on designing AI workflow and end-to-end pipelines
Collaborate with data scientists to create scalable ML solutions for business problems
Designing and developing machine learning and deep learning systems
Designing ML systems
Researching and implementing ML algorithms and tools
Undertaking machine learning experiments and test
Developing deep learning systems based on business needs

Skills

Programming skills
ML frameworks — PyTorch and Tensorflow
Distributed computing tools
Software engineering and system design skills
Data modeling and data architecture

Data Engineer

Data analysts and data scientists won’t be able to do their jobs without any data to work. This is why data engineers play the most crucial role in a data team.

Data Engineers are primarily responsible for providing data in a usable form for analytics and ML teams across the organization.

How do they do that, you ask?

They create a data pipeline, which is a set of technologies that form a specific environment where data is obtained, stored, processed, and queried. (source)

Using distributed computing, workflow, orchestration tools, stream processing, and other tools, they provide a reliable and easy-to-use system for ingesting and processing data, helping the data team build data-intensive applications successfully.

Read why Data Engineering is popular now in our recent article.

Roles

Create, monitor, and maintain data pipelines
Design, build and launch highly efficient and reliable data pipelines
Maintain the health of the data ecosystem by configuring monitors, defining alerts on common failure points, and giving feedback on data quality to data owners and business partners
Design data lake storage and access patterns to match customer requirements and conform to naming standards
Leverage data and business principles to solve large-scale web, mobile, and data infrastructure problems.
Partner with leadership, engineers, program managers, and data scientists to understand data needs.

Skills

SQL
Programming skills — Java, Scala, Python
Distributed Computing — Hadoop, Hive, Spark
Workflow and orchestration tools — Airflow, Luigi
Stream processing — Kafka
ETL and ELT tools
Databases — SQL and NoSQL
Data modeling
Cloud platforms — AWS, GCP
Data quality and validation
Designing and implementing pipelines

Summary

To summarize, here are the data roles and their responsibilities

Business analyst — Analyze the structure of a business, identify problems within it, and then improve the business processes.
Data Analyst — Analyze data to identify trends and patterns, define key metrics, and communicate them effectively with dashboards
Data Scientists — Apply statistical techniques and machine learning to data to answer business questions or build a product.
ML Engineer — Train, monitor, and maintain machine learning services.
Data Engineer — Gather, organize, and maintain data for the company with data pipelines

That’s all for this article, thank you for reading, and I hope you learned something new about what it takes to work in the data profession!

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