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Deciding Between Data Engineering and Analytics Engineering: Discover Your Ideal Career Path

Navigating a Crossroads in Data Careers

A little over a year ago, I was at a crossroads in my data engineering career. Armed with a business degree, I yearned for a role that would let me flex my creativity, engage in strategic decision-making, and delve into the business side of things. I wanted a role that would challenge me and allow me to grow in new ways.

Enter: The Analytics Engineer

After much searching, I stumbled upon a position that seemed to fit my desires perfectly: the analytics engineer. This role offered the perfect balance of technical prowess and business acumen, as well as the opportunity to collaborate across various teams in a company. I applied, got the job, and never looked back!

If you, too, find yourself unsure about which data career path to choose, this article will help you understand the key differences between data engineering and analytics engineering.

Data Engineers: Building the Infrastructure

A data engineer focuses on constructing the infrastructure that supports data storage and movement. Their tasks often include writing data pipelines in Python, using Spark for data processing, and deploying infrastructure using cloud technologies like AWS. They typically work closely with software engineers and other developers.

Key Skills for Data Engineers:

  • Python
  • AWS
  • Git
  • Bash
  • Spark
  • Hadoop

Analytics Engineers: Bridging Data and Business

In contrast, an analytics engineer works with data itself, transforming and moving it so it can be easily analyzed and visualized by data analysts or business users. They ensure data is ingested, transformed, scheduled, and ready for analytics. An analytics engineer is often responsible for choosing and setting up ETL/ELT tools and is a key player in the "modern data stack."

Key Skills for Analytics Engineers:

  • SQL
  • Experience with dbt
  • Communication
  • Python
  • Familiarity with modern data stack tools (Snowflake, Google Big Query, Fivetran, Matillion, Airbyte, Looker, ThoughtSpot, etc.)

Comparing Data and Analytics Engineering Roles

While both roles involve working with data, they each require different skill sets and responsibilities. Here are some key differences to consider when deciding which path to take:

SQL and dbt

Analytics engineers use SQL daily to write data transformations, and they often work with dbt, a leading data transformation tool. Strong SQL skills are a must for this role.


Both data and analytics engineers should be proficient in Python. While analytics engineers don't need to be master coders, they should be comfortable with basic Python programming for data pipeline tasks.

Modern data stack tools

Analytics engineers should be familiar with popular modern data stack tools for ingestion, transformation, warehousing, and deployment. They must be able to quickly learn and adapt to new tools and technologies.


Effective communication is crucial for analytics engineers, as they frequently interact with business teams and data analysts. They need to keep stakeholders informed about data status and quality, as well as understand the business's needs.

Choosing the Right Path for You

Shall I Choose Data Engineering or Data Analyst?

Here are some tips to help you decide which career path suits you best:

Opt for Analytics Engineering if:

  • You have a solid understanding of business concepts and enjoy solving business problems.
  • You love learning and working with new tools and technologies.
  • You prefer being proactive and creating your own path rather than simply solving assigned problems.

Choose Data Engineering if:

  • You enjoy collaborating with other engineers and expanding your developer skills.
  • You're more interested in data infrastructure and coding than extracting insights from data.
  • You are confortable about learning the latest programming languages.

Deciding on the Right Career Path in Data: Analytics vs. Data Engineering

Both data engineering and analytics engineering offer rewarding career paths with a strong demand for skilled professionals. To make an informed decision, consider the following factors:

  1. Passion: Think about which aspects of working with data excite you the most. If you are more interested in the technical aspects, like building and maintaining data pipelines, data engineering might be the right choice. If you prefer to work with business insights and use data to solve problems, analytics engineering could be a better fit.

  2. Skill Set: Assess your current skill set and the areas you wish to develop further. Data engineering typically requires strong coding and programming skills, while analytics engineering focuses more on data analysis and visualization tools.

  3. Work Environment: Consider the type of work environment and team you'd like to be part of. Data engineers often work closely with developers and may have more opportunities for collaboration, while analytics engineers may work more independently or with a smaller team focused on business insights.

  4. Job Market: Research the job market in your area or the regions you're interested in working. Compare the demand and salaries for data engineering and analytics engineering positions to make an informed decision about your career path.

Ultimately, the best choice for your career will depend on your personal interests, skills, and goals. Both data engineering and analytics engineering offer ample opportunities for growth and learning, so choose the path that aligns with your passion and strengths.


How Will AI Impact Your Career Choice?

If you want to get into a career in Data Analytics or Data Engineering, you cannot miss the latest development. How will AI impact your career? Will ChatGPT take over your data analyst or data engineering career? Everything seems to be undecided, involving and challenging.

Fortunately, there is a rising tool within the Open Source community that might save the day. RATH (opens in a new tab) brings the power of AI to every data analyst and data engineer. No more worrying, you are the beneficiary of AI.

Watch this demo video, on how you can use ChatGPT-powered RATH to generate a Data Visualization in less than 5 seconds, together with human-readable insights.

For another example, you can plugin RATH into AirTable, and create beautiful data visualization with minimal effort.

Sounds good? What's even better, RATH is Open Source (opens in a new tab). Check out the Open Beta Version now:

ChatGPT + RATH, Get Data Insights with One Prompt (opens in a new tab)


In summary, both data engineering and analytics engineering offer promising career paths in the world of data. By understanding the key differences between these roles and evaluating your personal interests, skills, and goals, you can make an informed decision and embark on a rewarding career that aligns with your passion. Keep in mind that the demand for skilled professionals in both fields is high, so either choice will provide ample opportunities for growth and success.