Data Engineer vs Analytics Engineer: How to Choose the Right Career
I was feeling stuck in my work as a data engineer a little over a year ago. I had majored in business in college and wanted to get more involved with that side of things. I enjoyed my work as a data engineer, but I wished for more freedom and creativity. I preferred to be active in business decisions rather than have my work decided for me. As a data engineer, I felt like I was doing duties that had already been strategically determined, rather than having a role in prioritizing business needs.
I began applying for several data engineering positions, seeking ones that covered more of the day-to-day duties that I wanted to be involved in, such as strategic data decisions, warehouse architecture, and data model automation. As you might expect, it was difficult to locate a data engineering post that also provided business exposure and wasn’t solely focused on back-end IT development. I began looking into analyst positions as well but discovered that they didn’t require as many of the technical abilities that I had worked so hard to obtain over the previous three years of my career. I was incredibly disheartened, believing that there was no work out there that I would enjoy while also challenging me in the ways I desired to grow.
“Then I came across a job posting for an analytics engineer. I had never heard of this position before, but it seemed precisely like what I was searching for after reading the job description. It included both technological and business topics. It necessitated coordination across several teams inside a corporation. While continuing working in SQL and Python and increasing my coding skills, I was able to communicate with the growth and marketing teams.”
I applied for the position and have been working as an analytics engineer for about a year. I’ve never looked back!
If you’re in a similar situation, unsure of which direction to take with your data career, I hope this helps you have a better grasp of the technical opportunities available. Here’s a breakdown of my responsibilities as a data engineer and an analytics engineer.
What is the role of a data engineer?
Let us begin by discussing the role of a data engineer. A data engineer is someone who creates the infrastructure that allows data to be stored and moved. They are not concerned with the data itself, but rather with how they might support the data. This covers writing data pipelines in Python, processing data in Spark, and deploying infrastructure on cloud platforms such as AWS. A data engineer collaborates with other developers, such as software developers.
As a data engineer, I first concentrated entirely on DevOps and AWS infrastructure deployment. To monitor and deploy code, I used various pipeline tools such as Data Dog and Jenkins. I built numerous Python scripts to clean up our AWS setups and deploy EC2 clusters.
However, in my second year, I focused more on SQL and dbt. This was unusual for data engineers, who are used to working with software development languages and cloud platforms like AWS and Azure. It all depends on the organization you work for and how their responsibilities are labeled. Still, I concentrated on developing programs such as Python APIs to aid with data collecting. Again, I was focused on infrastructure and processing tools rather than the data itself.
Top skills required for data engineering:
Python
AWS
Git
Bash
Spark
Hadoop
A data engineer’s day-to-day involves meeting with a scrum master to assist in prioritizing your activities and moving you along. Instead of interacting with business teams, this type of middleman usually tells you what should be prioritized. Then, you can meet with other data engineers or development teams to decide how to build your data processes. This comprises the design as well as the specifics of what must be deployed. Much of the day is spent with your head down, focusing on your code and what you’re building. Most days, I’d be building a Python script to speed up our deployments or to test infrastructure modifications before they were implemented.
What is the role of an analytics engineer?
While many people understand what it takes to be a data engineer, let’s talk about what it takes to be an analytics engineer. Your technological skills will be put to use, but for a very different cause. You should also expect to use more of your business acumen, analyzing data to decide the organization’s path.
First, what exactly is an analytics engineer? An analytics engineer is someone who transports and modifies data from its source so that the data analyst or business user may readily evaluate, visualize, and act on it. Analytics engineers deal with both the data and the movement of data. It is their responsibility to ensure that data is ingested, converted, scheduled, and ready for analytics. The “modern data stack” is the brainchild of many analytics engineers. They choose which tools to utilize for ETL/ELT and then configure them.
Top skills required for analytics engineering:
SQL
Experience with dbt
Communication
Python
Experience with modern data stack tools (Snowflake, Google Big Query, Fivetran, Matillion, Airbyte, Looker, ThoughtSpot, etc.)
Data Engineer vs Analytics Engineer: How to Choose the Right Career
I was feeling stuck in my work as a data engineer a little over a year ago. I had majored in business in college and wanted to get more involved with that side of things. I enjoyed my work as a data engineer, but I wished for more freedom and creativity. I preferred to be active in business decisions rather than have my work decided for me. As a data engineer, I felt like I was doing duties that had already been strategically determined, rather than having a role in prioritizing business needs.
I began applying for several data engineering positions, seeking ones that covered more of the day-to-day duties that I wanted to be involved in, such as strategic data decisions, warehouse architecture, and data model automation. As you might expect, it was difficult to locate a data engineering post that also provided business exposure and wasn’t solely focused on back-end IT development. I began looking into analyst positions as well but discovered that they didn’t require as many of the technical abilities that I had worked so hard to obtain over the previous three years of my career. I was incredibly disheartened, believing that there was no work out there that I would enjoy while also challenging me in the ways I desired to grow.
“Then I came across a job posting for an analytics engineer. I had never heard of this position before, but it seemed precisely like what I was searching for after reading the job description. It included both technological and business topics. It necessitated coordination across several teams inside a corporation. While continuing working in SQL and Python and increasing my coding skills, I was able to communicate with the growth and marketing teams.”
I applied for the position and have been working as an analytics engineer for about a year. I’ve never looked back!
If you’re in a similar situation, unsure of which direction to take with your data career, I hope this helps you have a better grasp of the technical opportunities available. Here’s a breakdown of my responsibilities as a data engineer and an analytics engineer.
What is the role of a data engineer?
Let us begin by discussing the role of a data engineer. A data engineer is someone who creates the infrastructure that allows data to be stored and moved. They are not concerned with the data itself, but rather with how they might support the data. This covers writing data pipelines in Python, processing data in Spark, and deploying infrastructure on cloud platforms such as AWS. A data engineer collaborates with other developers, such as software developers.
As a data engineer, I first concentrated entirely on DevOps and AWS infrastructure deployment. To monitor and deploy code, I used various pipeline tools such as Data Dog and Jenkins. I built numerous Python scripts to clean up our AWS setups and deploy EC2 clusters.
However, in my second year, I focused more on SQL and dbt. This was unusual for data engineers, who are used to working with software development languages and cloud platforms like AWS and Azure. It all depends on the organization you work for and how their responsibilities are labeled. Still, I concentrated on developing programs such as Python APIs to aid with data collecting. Again, I was focused on infrastructure and processing tools rather than the data itself.
Top skills required for data engineering:
A data engineer’s day-to-day involves meeting with a scrum master to assist in prioritizing your activities and moving you along. Instead of interacting with business teams, this type of middleman usually tells you what should be prioritized. Then, you can meet with other data engineers or development teams to decide how to build your data processes. This comprises the design as well as the specifics of what must be deployed. Much of the day is spent with your head down, focusing on your code and what you’re building. Most days, I’d be building a Python script to speed up our deployments or to test infrastructure modifications before they were implemented.
What is the role of an analytics engineer?
While many people understand what it takes to be a data engineer, let’s talk about what it takes to be an analytics engineer. Your technological skills will be put to use, but for a very different cause. You should also expect to use more of your business acumen, analyzing data to decide the organization’s path.
First, what exactly is an analytics engineer? An analytics engineer is someone who transports and modifies data from its source so that the data analyst or business user may readily evaluate, visualize, and act on it. Analytics engineers deal with both the data and the movement of data. It is their responsibility to ensure that data is ingested, converted, scheduled, and ready for analytics. The “modern data stack” is the brainchild of many analytics engineers. They choose which tools to utilize for ETL/ELT and then configure them.
Top skills required for analytics engineering:
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