Analytics Engineers: 6 Critical Responsibilities

Talha • Last Modified: December 29th, 2022

Feature image

Back in the days when on-premise SQL Databases were the backbone of ETL (Extract, Transform, and Load) systems, there were only two relevant roles in an organization’s Data Landscape – Database Developers and Business Analysts. With the advent of Big Data and Deep Learning, Database Developers were replaced by Data Engineers, and a new role called Data Scientists was introduced.

With the increase in processing power of Data Warehouses and the emergence of Massively Parallel Processing Cloud-Based Warehouse services, it was no longer required for Data to be in the transformed form when it was loaded into the Data Warehouse. This led to the advent of the ELT (Extract, Load, and Transform) paradigm – and a new role – Analytics Engineers.

This article is about who Analytics Engineers are and what are their Key Roles and Responsibilities. This article also provides a detailed overview of the skills you would require to become an Analytics Engineer.

Table of Contents

What is an Analytics Engineer

Introduction to Analytics Enginners image
Image Source

In the era of big data, the world is producing more information than it can consume. Every minute of the day:

  • Users share 240,000 photos on Facebook.
  • Customers spend $283K on Amazon.
  • People submit 5.7 million queries to Google.
  • 6 million customers make an online purchase.
  • Viewers stream a cumulative 452,000 hours of Netflix.

This data is critical for organizations to know their customer needs and take strategic decisions to improve their business performance. To effectively extract, transform, load, and then gain insights from this data, there has been an enormous demand for several data-associated roles such as an Analytics Engineer.

The primary reasoning behind the emergence of the role is the shift towards ELT. This shift was a result of the evolution of Data Warehouse Technologies and Cloud-Based Data Engineering Tools. In the case of the ETL model, all phases of the process- Extract, Transform and Load – were handled by Data Engineers.

When Massively Parallel Processing Data Warehouse frameworks came in, they eliminated the need for data to be Transformed before Loading. Because of their quick processing abilities and rich SQL Layer, it was possible to generate transformed data when required. 

The next critical development that aided this shift was the emergence of Cloud-Based ETL tools like Hevo Data. Such tools made it possible for people with little infrastructure engineering skills to take control of Data Transformations and create reusable Data Assets. This aspect was previously handled by Data Engineers who were in charge of both setting up the infrastructure as well as transformation jobs.

In the ELT Landscape, Data Engineers were reduced in setting up the Data Infrastructure for Extraction and Load while the Analytics Engineers were exclusively put in charge of setting up the transformation jobs and maintaining the Data Assets. 

To summarize everything, the modern ELT Landscape has 4 broad roles in managing data.

  • Data Engineers: Data Engineers are responsible for setting up the Data Infrastructure and ensuring the availability of data.
  • Analytics Engineers: Make use of the infrastructure and environment created by Data Engineers and create reusable Data Assets. 
  • Data Scientists: Data Scientists apply Machine Learning Algorithms to derive value from the data. They mostly consume the Data Assets created by Analytics Engineers to create actionable insights.
  • Business Analysts: Business Analysts work closely with the Stakeholders to capture requirements for building Dashboards and reports that help in Decision Making.

Comparison between Data engineers, Analytics Engineers, Data Scientists, and Business Analysts

Analytics Engineers: Comparison

For more information on Analytics Engineers, click here.

Simplify ETL with Hevo’s No-code Data Pipelines

Hevo Data, a No-code Data Pipeline helps to Load Data from any data source such as Databases, SaaS applications, Cloud Storage, SDK,s, and Streaming Services and simplifies the ETL process. It supports 100+ data sources and is a 3-step process by just selecting the data source, providing valid credentials, and choosing the destination. Hevo loads the data onto the desired Data Warehouse and enriches the data and transforms it into an analysis-ready form without having to write a single line of code.

Its completely automated pipeline offers data to be delivered in real-time without any loss from source to destination. Its fault-tolerant and scalable architecture ensure that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data. The solutions provided are consistent and work with different Business Intelligence (BI) tools as well.

Check out why Hevo is the Best:

  • Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
  • Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
  • Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
  • Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
  • Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
  • Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
  • Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.

Simplify your Data Analysis with Hevo today! Sign up here for a 14-day free trial!

Responsibilities of Analytics Engineers

Given below are the responsibilities:

1) Building Reusable Data Assets

The primary responsibility is to build Data Assets using the infrastructure and environment set up by the Data Engineers. They write transformation jobs to generate such Data Assets. The code that was used to generate the Data Asset is as important as the Resultant Data Set.

So maintaining the code by making use of the best Software Engineering Practices and implementing processes for seamless deployment of the code also comes under the purview of Analytics Engineers. Analytics Engineers can create these Data Assets based on their own conviction of requirements for assets or from the requirements that come from Stakeholders. 

2) Ensuring the Data Access

Another important aspect is to ensure the Data Assets are available for everyone who needs it. This involves documenting the Data Assets and exposing them in a way that it is easy for data customers to find them. Documentation generally includes the lineage of the data set and the details of the features that constitute the data. Each feature needs to be documented as detailed as possible. Most data consumers will search the data based on these features.

In most organizations, you will find a third-party or custom tool that enables the customers to search for Data Assets. It is the job of the Analytics Engineers to ensure these search tools get populated with the details of the newly created assets.

3) Maintaining the Data Assets

Developing the code to generate Data Assets is only the first part of the job of Analytics Engineers. For the data to be of any value to the organization, it needs to be properly updated and monitored for any errors. Knowledge about the freshness of the data is critical to the people who consume it. Analytics Engineers often define standards to communicate the freshness of their data. It is also their responsibility to ensure that the jobs that create these assets run without problems and to implement an escalation process for Data Engineers in case anything goes wrong.

4) Defining the Quality Standards for Data

The quality of the data is an important factor that dictates the value it can provide to a customer. Analytics Engineers are not only tasked with maintaining the quality but also responsible for defining the quality standards of the data. This includes setting definitions of done metrics for Data Engineers and minimum acceptance criteria for the sources that they consider.

They often write scripts and cleansing algorithms to further refine the data that they are responsible for. These Data Quality Metrics are often used to evaluate the performance of an Analytics Engineering Team and also help them to track the effectiveness of their quality improvement efforts.

5) Collaborating with Business Analysts and Data Scientists

As evident from the details above, Analytics Engineers work between Data Engineers, Business Analysts, and Data Scientists. Maintaining the relationship between each of these Stakeholders is an important part of their job. Data scientists and Business Analysts are the primary customers of the assets created by Analytics Engineers. They are the ones who define the requirement for Analytics Engineers.

In most cases, Data Scientists and Business Analysts will only know the result they are trying to accomplish and it becomes the responsibility of Analytics Engineers to keep asking questions to arrive at the exact Data Asset requirement. In some cases, there is also a bit of coaching involved in helping them accurately express their requirements. Such conversations often result in Analytics Engineers defining a format for future asset requirements and requests.

6) Optimizing Transformation Workflows

When increasingly Data Assets get requested, the usual process is to keep implementing transformation jobs to create data assets. This can result in a non-optimal transformation job portfolio. Analytics Engineers have the responsibility to keep Analyzing the transformation job portfolios and to explore possibilities for optimizing them such that no opportunity for combining jobs for better performance or maintainability is missed.

Skills Required for Analytics Engineers

Having defined the Roles and Responsibilities and the people they interact with, we are now in a position to define the skills that Analytics Engineers should possess. Following are the 6 skills required for becoming an Analytics Engineer:

1) Experience in Working with Data

Working with Data image
Image Source

The most important skill that an Analytics Engineer should have is a penchant for data and the knowledge of how data evolves in an organization. This means he should come from a background of working with data. Analytics Engineer being a new role, it is difficult to get people with experience in that role. Normally people from Data Engineers who are more into the data side, transition to Analytics Engineering once the Data Pipeline Architecture is settled.

2) SQL Mastery

SQL logo
Image Source

A large part of an Analytics Engineers’ job will be exploring data sets and creating logic for transformations. And it is not a surprise that SQL will be the best friend for an Analytics Engineer.

3) Programming Language Expertise

Top Programming Languages for Analytics image
Image Source

Expertise in one Programming Language is a must for Analytics Engineers. Transformation logic can be represented in a much more concise way if one is open to use a Programming Language to do so. Programming Languages are also required in setting up the jobs and Automating the Data Management. A language like Python, which is known to have an advantage in working with Scala is preferred.

4) Interpersonal Skills

Interpersonal Skills image
Image Source

As evident from the responsibility section, interacting with Stakeholders and the ability to ask the right questions are very important for the success of Analytics Engineers. They should have the right Interpersonal Skills to interact with Stakeholders from multiple teams with different priorities.

5) Knowledge of Data Engineering Tools

Data Engineering Tools image
Image Source

Experience in working with Data Engineering Tools is an advantage for Analytics Engineers. Exposure to the Cloud-Based Data Warehouses like Amazon Redshift, Snowflake, and Cloud-Based ETL Tools like Hevo Data, AWS Glue, etc. are an added advantage. Business Intelligence Tools may also be required in daily activities.

6) Knowledge of Software Engineering Practices

Software Engineering Practices image
Image Source

Beyond the skills mentioned above, an Analytics Engineer is also considered as a Software Engineer and will need to use standard Software Engineering Practices like Version Control, Auto-Deployment Processes, etc. 


This article provided a detailed overview of the Roles and Responsibilities of Analytics Engineers and the people who generally work with Analytics Engineers (Data Engineers, Data Scientists, and Business Analysts). In the modern ELT Landscape, Analytics Engineer is a very critical role and act as a bridge between the producers and the consumers of the data. A Cloud-Based ETL Tool like Hevo Data can make the job of an Analytics Engineer easier by enabling him to build Reusable Data Assets in no time.

In case you want to integrate data into your desired Database/destination, then Hevo Data is the right choice for you! It will help simplify the ETL and management process of both the data sources and the data destinations.

Want to take Hevo for a spin? Sign up here for a 14-day free trial and experience the feature-rich Hevo suite first hand.

Share your experience of understanding the Roles and Responsibilities of Analytics Engineers in the comments section below!

No-Code Data Pipeline For Your Data Warehouse