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What Is An Analytics Engineer? Everything You Need to Know

This article provides an overview of an up-and-coming role in data, the analytics engineer. We explore what is meant by analytics engineering, what are the differences between analytics engineering and traditional data roles like data scientist, data engineer, and data analyst, and what is a learning path for analytics engineering.
Jun 2022  · 10 min read

Data science is continuously evolving, and so are the job titles and roles within any modern data team. During the early days of data science, many job titles contained the term “big data”. 

Over the past two years, as machine learning became more and more operationalized, MLOps began appearing in different job titles. And today, as organizations grow in their data literacy and analytics maturity, we’re seeing the rise of the analytics engineering role. 

In a nutshell, analytics engineers act as a bridge between engineering and analyst functions.  Their role is to apply engineering best practices to provide clean, transformed datasets, that are ready for analysis. 

This article aims to demystify what an analytics engineer is and what they do, as well as compare the role with other common data roles, and provide resources to break into analytics engineering. 

What is An Analytics Engineer? 

Dataform provides a fantastic analogy for how analytics engineers fit into the wider data team, using a familiar example—cupboards. Keep this analogy in mind as we consider why analytics engineers are on the rise.

“Data engineers build the cupboard, they gather together the wood and the tools and put it together. The Analytics Engineers open the cupboard and start putting in the plates, mugs, and bowls, and arrange them in a certain order. This could be arranging them into particular colors, shapes, or sizes. Data analysts then go into the cupboard and they know where everything lives as it is arranged nicely. They can then grab the small blue mug they were looking for and go make a cup of tea!”

Josie HallProgram Manager at Google

Before the rise of analytics engineering, data analysts would use visualization tools such as Tableau or Power BI to provide insights to stakeholders. These tools are excellent ways to present data but are not the best ways to transform and store data. 

Imagine a scenario where a data engineer deploys a data pipeline that loads marketing data, but the data quality is lacking. Only the data analysts within the marketing team have the domain knowledge to transform and improve the quality of the data. However, they won’t use the same technology stack as the data engineer. 

Meanwhile, data engineers don’t have the domain knowledge to quickly understand what transformations are needed and are most likely inundated with higher priority tasks coming from across the business. As a consequence, analysts may have to transform the data in Tableau or Power BI by building their own temporary table in a relational database. This results in inefficient, slower times to insight. 

Enter the analytics engineer. The analytics engineer sits between the data analyst and the data engineer. In the scenario above, they would have the technical skills to optimize data transformations, alongside domain knowledge. 

By working closely with the data analyst, the analytics engineer can deliver clean data for the data analyst to use by transforming the data with the appropriate tools and technologies. 

How are Analytics Engineers Different From Other Data Roles? 

The lines between the different roles are blurry, so how do analytics engineers differ from other data roles, and do they work together?

Analytics Engineer vs Data Analyst

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. Data analysts are typically highly skilled in SQL and business intelligence tools like Power BI or Tableau, with limited use of tools like R or Python.

Analytics engineers work with data analysts to optimize data models that are ready for consumption. They are also responsible for maintaining documentation around the data, which enables data analysts to find insights more quickly. 

Analytics Engineer vs Data Engineer

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. 

Admittedly, this definition has a high level of overlap with the analytics engineering role. However, by referencing the cupboards example again, data engineers are responsible for ensuring that analytics engineers have the right data sources to organize and model for consumption for the data analysts of a data team. 

Moreover, data engineers are responsible for setting up custom APIs and ETL pipelines from proprietary sources — whereas data engineers focus a lot more of their time on improving pipelines from vendor APIs like Google Analytics. 

Analytics Engineer vs Data Scientist

Data Scientists investigate, extract, and report meaningful insights into the organization’s data. They communicate these insights to non-technical 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 like SQL, Python, and R, conduct analysis and often work with big data tools. 

The relationship between data scientists and analytics engineers is similar to the one between data analysts and analytics engineers. Analytics engineers enable data scientists to focus more on insights and less on cleaning and optimizing datasets. 

Analytics Engineer vs Machine Learning Engineers

Machine Learning Engineers 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 and are much more technology-focused than their counterparts. 

Analytics engineers are much more focused on enabling data analysts and data scientists, who are responsible for delivering insights to business stakeholders. 

Analytics Engineer Job Comparisons

Data Analyst

Data Engineer

Data Scientist

Machine Learning Engineer

Analytics Engineer

Analyze data and report insights to stakeholders

Build dashboards that are consumed by the wider organization

Leverage subject matter expertise and domain knowledge for recommendations

Build and maintain ETL data pipelines using vendor and proprietary APIs

Optimize and centralize data in a data lake or warehouse 

Deliver data into machine learning pipelines 

Process data in the cloud 

Analyze data and report insights to stakeholders

Design experiments such as A/B tests 

Deploy dashboards that are consumed by the wider organization

Develop supervised and unsupervised learning workflows 

Analyze non-standard data types such as time-series, text, geospatial, or image data

Train and deploy machine learning models 

Monitor and improve the performance of machine learning models in production

Apply software engineering best practices to the machine learning workflow (CI/CD)

Optimize pipelines built by data engineers for consumption

Apply engineering best practices to data models used by data analysts and scientists 

Develop, standardize and improve documentation on data

R or Python

SQL, Power BI, Tableau

R or Python


Git, Shell and command-line tools

Big data tools like Airflow or Spark

Cloud-based tools like AWS, Azure, GCP, or Snowflake

R or Python


Git, Shell, and command-line tools

Big data tools like Airflow or Spark

R or Python


Git, Shell, and command-line tools

Big data tools like Airflow or Spark

R or Python


Git, Shell, and command-line tools

Cloud-based tools like AWS, Azure, GCP, Snowflake or dbt

Analytics Engineer Salaries

The role of the analytics engineer is nascent, which means there are few people on the market with the exact blend of engineering and analytics skills required to succeed in this role. This makes the analytics engineer role highly attractive from a compensation point of view. Below are some of what you can expect in terms of salary ranges for analytics engineering roles in the United States. 

  • Glassdoor: According to Glassdoor, Analytics Engineers make on average $91,080 per year. Moreover, depending on location and company, salaries can range up to $208,000 per year.
  • Comparably: According to Comparably, Analytics Engineers make on average $100,305 per year. Moreover, depending on location and company, salaries can range up to $185,000 per year.

While the numbers above represent rough ranges from analytics engineering roles across the spectrum, it’s also worth noting that highly mature data companies like Netflix pay at the top range of the personal compensation market for data roles. For example, according to levels.FYI, Netflix Analytics Engineers earn up to $375,000 per year. 

How to Become an Analytics Engineer? 

As data roles become specialized, upskilling paths are becoming more narrow as well. Unlike many of the data roles discussed above, analytics engineers require a broad skill set that requires them to learn tools and concepts such as the following 


SQL is arguably one of the most widely used tools in all of the analytics across any data role. Luckily, it’s also one of the easiest to learn and master. Check out this article to see how to learn SQL, alongside additional learning resources here. 


Python is the defacto most popular programming language there is at the moment. Whether entering into analytics engineering roles, or any data role mentioned above, Python will surely be useful. Find all the resources you'll need to learn Python here and get started with the following courses.

ETL Tools

ETL stands for “Extract, Transform, and Load”. These are tools that allow engineers to set up data pipelines that extract data from different sources, transform it into consumable data, and load them into databases. One of the most popular open-source ETL tools is Airflow. Check out Airflow in action in this course.

Cloud Computing Tools

While the term “cloud computing tools” is definitely an umbrella term, analytics engineers and other data roles alike leverage cloud computing services such as AWS, Azure, Google Cloud, or Snowflake on a regular basis. These tools allow data teams to store, process, and deploy data & data solutions in the cloud. The most popular cloud computing tool is AWS. You can learn more about AWS by checking out DataCamp’s AWS Courses listed below.

Version Control

Version control is arguably the backbone of software engineering best practices. In a nutshell, it allows practitioners to keep track of what they did when, undo any changes they decide they don't want, and collaborate at scale with others. 

Command-line tools like Git let you apply version control best practices. Learn more about Git by checking out this cheat sheet

Communication Skills

While every data role requires communication skills to some degree, analytics engineering requires the same level of communication skills as a data analyst role would require, alongside the technical chops of a data engineer. Becoming a better communicator is a skill, not a talent. Check out Data Communication Concepts for improving your technical communication skills. 

Become an Analytics Engineer 

Analytics Engineering, just like MLOps, is extremely nascent. To keep ahead of the curve, check out the resources below. 

Introduction to Python

4 hours
Master the basics of data analysis with Python in just four hours. This online course will introduce the Python interface and explore popular packages.
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Intermediate SQL Queries

4 hours
Master the basics of querying tables in relational databases such as MySQL, SQL Server, and PostgreSQL.

Introduction to Git

4 hours
This course is an introduction to version control with Git for data scientists.
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