Core 4 Data Roles
What are the differences, anyway?
If you’re confused about the different data roles and which one is the best fit for you, that’s ok. In fact, it’s completely normal!
When I first realized I wanted a career in data, I wanted to be a data scientist. I was interning at a fashion company at the time, and someone in the office had introduced me to the idea. I loved math and solving problems- it seemed like the perfect fit!
In my pursuit of data science, Capital One recruited me to join a 6-month coding bootcamp that transitioned into a full-time data engineering role. Instead of attending a data science graduate program, I accepted the offer. I worked as a data engineer for two years before realizing it wasn’t quite the right fit for me.
While working as a data engineer, I came to the realization that I wanted to be a data analyst. I missed interacting with the business and closely following how my work impacted it. Unfortunately, I didn’t pass the interview process for the data analyst role, leading me on a search for a data analyst role outside of Capital One.
While browsing data analyst roles on LinkedIn, I came across a role for an analytics engineer. The requirements were a perfect blend between the skills I had as a data engineer and the experience I sought as a data analyst. Ever since that moment, I’ve been sharing my journey learning the ins and outs of analytics engineering.
In this article, I’ll discuss the main differences between the “Core 4” data roles and how to know which one is the right one for you. Be sure to keep reading for an example of each of these roles in action when solving for customer churn at our chocolate chip subscription company, Choco Chippy!
Data Analyst
Data analysts work closely with business stakeholders to define metrics and the different dimensions to slice those metrics by. They are typically focused on a particular area of the business like product, growth, or finance.
Super power: Making the most of the data available to them to get insights to business questions fast!
Technical skills:
SQL
Excel
BI tools like Looker, Tableau, Power BI, etc.
A/B testing and experimentation
Data analysts don’t focus on loading data, testing data quality, or transforming it within the warehouse, but rather find a way to use the data available to them by creating dashboards and reports in BI tools and using SQL to produce ad-hoc queries.
Who thrives?
you enjoy getting deep into business problems and thinking about how you can use what you have to get stakeholders what they need
you don’t mind working fast and scrappy
you enjoy visualizing data in an easy-to-understand way
you are an excellent communicator and easy to work with
you deeply understand how the business functions
Data Engineer
Data engineers set a stable foundation for data systems. They build infrastructure to support ETL pipelines (typically using Python), focusing on data quality from ingestion to orchestration. They deploy and manage cloud infrastructure that supports data tooling.
Super power: Creating stable infrastructure that ensures performant, quality analytics pipelines.
Technical skills:
Python
SQL
Cloud tools like AWS, Azure, and GCP
Database management
Data warehouse performance (indexes, partitions, etc.)
Data engineers don’t necessarily interact with the business but create a stable foundation for analytics engineers to build business solutions. They closely work with other engineers to define contracts on expectations around the source system data.
Who thrives?
you enjoy heads-down focus work solving deeply technical problems
you aren’t scared of data fires and face them head-on, ready to get to the root cause
you like to build, build, build and tinker and tweak
you’re ok with not seeing immediate impact from your work
Data Scientist
Data scientists build predictive/ML models (which are different than data models!) and find statistically significant patterns in large-scale datasets. They have strong statistical knowledge, as all of the data work they do is centered around this.
Super power: Uncovering hidden patterns in data, building off of them in a way that allows the business to predict future behaviors.
Technical skills:
Python (libraries such as NumPy, Pandas, ScikitLearn, Matplotlib)
R
basic SQL for data cleaning
Like data analysts, data scientists work cross-functionally to understand the needs of the business. While they do a lot of deep research-like work, they also focus on the value it will have for their stakeholders.
Who thrives?
you are a statistics nerd and love applying best practices to everything that you do
you don’t mind messy data and understand 90% of your time may be spent cleaning it
you have a strong understanding of how your findings can drive impact
Analytics Engineer
Analytics engineers own the analytics pipeline. They build dimensional data models from start to finish, documenting and testing them. They architect the data warehouse for analytics use cases, managing governance and cost, while also managing ETL pipelines.
Super power: Taking business requirements and translating them into scalable, reusable data models.
Technical skills:
SQL
dimensional data modeling
modern data stack tools like dbt, Fivetran, Snowflake, etc.
cloud data warehousing
Analytics engineers work closely with data analysts to understand what models need to be built to support stakeholder needs. Rather than being told what to build, they are pivotal in deciding what data models need to be built to fulfill the business’s needs.
Who thrives?
you have a knack for making technical concepts easy to understand
you are an A+ communicator, getting to the root of business problems
you enjoy working closely with data engineers to define expectations around the data
you work with scalability and quality in mind, always documenting your work and reflecting on how to make it better
Now let’s apply these roles to an actual business problem…
Let’s pretend we have a chocolate chip cookie subscription company called Choco Chippy. Choco Chippy has customers that pay us on a monthly or yearly basis to get chocolate chip cookies delivered to their homes every week.
Recently, we’ve been experiencing declining customer retention rates. To fix this, we need to understand why and predict which customers are at risk of churning.
Luckily, Choco Chippy has a data team of 4, each with their own unique set of skills. Together they can tackle the problem of declining retention rates!
Data engineer
The data engineer will collect customer behavior data from various sources (web app, Segment events, etc.). They will ensure the infrastructure is in place to deliver this data to the data warehouse, maintaining quality and consistency along the way.
Analytics engineer
The analytics engineer will then take all of the raw data sources relevant to customer retention and build a core data model that helps data analysts measure retention rates and analyze churn. To do this, they will work closely with the data analyst in talking to the business and understanding the processes that could lead to churn.
Data analyst
After working closely with the analytics engineer and understanding what the business needs, the data analyst will use the data model built to create custom dashboards displaying retention rates and churn reasons. They will meet with the business regularly to discuss any insights they’ve found from digging deeper into the retention model.
Data scientist
Choco Chippy’s data scientist would build a predictive model to identify at-risk customers before they actually churn. Using statistical methods and machine learning, they'd analyze patterns in data model built by the analytics engineer (and maybe other data sources as well) to create models that can score each customer's likelihood of churning in the next 30/60/90 days. This way, the business can proactively intervene with targeted retention campaigns rather than just reacting after customers have already left.
Together, the core 4 data roles save that day and identify that churn is due to crunchy cookies! Bringing these insights to the product team, Choco Chippy is then able to implement better quality control to ensure cookies stay chewy.
Looking to upskill your analytics engineering skills?
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Happy learning!
Madison