Data Insights
Article

5 Signs Analytics Engineering Might Be the Right Career For You

Madison Schott
January 30, 2023
10 min read
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You are a leader who paves the way by setting best practices and standards for your team.

If you like things done your way, analytics engineering is a great career for you. It's a career where you can take charge of standards being set in your data. Analytics engineers create a style guide to govern how models are built and how the data pipeline is set up. This style guide typically contains standards like how you should be naming your models and columns, best practices for making code changes, and how often your data pipeline should run.

Documentation is your best friend. Analytics engineers are the ones on top of data documentation within the engineering and data teams. They ensure data sources, data models, and dashboards are all properly documented so that the data environment is easy for everyone to navigate. Data tends to go undocumented and be quite messy without an analytics engineer, which you will probably see if you join as the first one on a team. 

You’re a stickler about good-quality data. 

Chances are if you care about standardization and documentation, you also care about data quality. Analytics engineers are the ones to test and validate data models, ensuring they meet high standards. They look into different factors like freshness, completeness, and accuracy to ensure downstream data models are not breaking. 

This is one of the most important duties of a good analytics engineer. Because, at the end of the day, if you have bad-quality data that is being used to make business decisions, those decisions aren’t going to have a great outcome. Bad data in equals bad data out. It’s important that you add tests in your data models to catch issues as soon as possible.

As part of data quality, it’s imperative that you properly validate all of your data models so that you can be confident in the KPIs you are producing. When you properly validate, business teams don’t have the option to second-guess the numbers they are seeing. This builds trust in the data, something that is surprisingly hard to have. 

You enjoy discovering new tools and ways of doing things. 

One of the coolest parts of being an analytics engineer is all the new tools you get to discover. The modern data stack is still a fairly new concept and new tools are popping up every day. The market is constantly evolving and I am always looking for new technologies to use for warehousing, ingestion, orchestration, testing, and observability. There is always something new disrupting the space!

Some of my favorite tools to discover are open-source ones. These are great because they are free, saving money for your company while also improving its data processes. Airbyte is an open-source data ingestion tool that I first discovered when looking for an affordable way to move data from external sources to my Snowflake data platform. I also love dbt for data transformation and the free packages that you can download with it such as re_data and dbt expectations. Another favorite is Prefect for orchestrating data pipelines using simple Python code. 

Finding new tools is like a treasure hunt. There is always something new to discover that can bring immense value to your company whether that’s through saving money, producing higher-quality data, or saving the team valuable time. So, if you like learning new things and constantly improving what you are building, analytics engineering could be for you!

Conclusion

Data engineers work on back-end infrastructure that helps collect data to be used by analytics engineers. Analytics engineers interact with business teams to define KPIs and produce data models with the right columns and metrics. Data analysts then use the datasets produced by these data models to create reports and dashboards for stakeholders to use. 

Analytics engineers own the following tasks:

  • Setting up the modern data stack from ingestion to orchestration using the appropriate tools 
  • Data transformation within data models 
  • Building data pipelines and orchestrating data models 
  • Setting and documenting standards across the data 
  • Maintaining high-quality data 
  • Working with business teams to properly define KPIs 

If these tasks interest you, or if you’ve done them in the past, you may be the perfect fit for analytics engineering! If you’re interested in learning more about the career, be sure to check out my new ebook “The ABCs of Analytics Engineering”, a learning resource that walks you through important concepts to know as an analytics engineer. This book will teach you everything from how to craft the perfect resume to what tests to implement for high-quality data. 

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