Data Engineering 101: The Three Types of Data Engineers You Need to Know

Data is everywhere. It’s in your phone, your laptop, your car, your fridge, and even your toaster. Data is what makes the world go round. But data is also messy, complex, and hard to handle. That’s why we need data engineers.

Data engineers are the superheroes of the data world. They are the ones who make data easy to use and understand for everyone else. They are the ones who build the systems and tools that collect, store, process, and analyze data. They are the ones who make data magic happen.

But not all data engineers are the same. Depending on what kind of company they work for and what kind of problems they solve, data engineers may have different roles and responsibilities. In this article, I will introduce you to three types of data engineer personas and what they might work on. This will help you understand what data engineering is all about and how to hire the right people for your team.

Data Platform/DataOps Engineer

This type of data engineer is like a builder. They build the foundations and structures that other data users rely on. They are in charge of setting up and managing the sources, systems, and tools that handle data. They also make sure that everything works smoothly and securely.

Data platform engineers need to be good at software engineering, cloud computing, distributed systems, and DevOps practices. They also need to know a lot about different data technologies and frameworks, such as Hadoop, Spark, Kafka, Airflow, AWS, Azure, etc.

Some examples of projects that data platform engineers might work on are:

- Creating a big data lake or warehouse that stores all kinds of data

- Developing a system that can ingest data from multiple sources and formats

- Building a tool that can check and fix data quality issues

- Creating a dashboard that can monitor and alert on data performance

- Automating data pipeline deployment and testing using CI/CD tools

Data platform engineers are the ones who make sure that the data house is in order. They are like the architects, engineers, and contractors of the data world. Without them, nothing would work.

Pipeline-Focused Data Engineer

This type of data engineer is like a plumber. They connect the pipes that move data from one place to another. They use the infrastructure and tools built by the data platform engineers. They know how the data is generated and what it means.

Pipeline-focused data engineers need to be good at data modeling, ETL/ELT, SQL, Python/Scala/Java, and performance tuning. They also need to be able to communicate well with business stakeholders and data analysts/scientists to understand their needs and expectations.

Some examples of projects that pipeline-focused data engineers might work on are:

- Developing a batch or streaming pipeline that transforms raw data into useful tables

- Integrating external or third-party data sources into the existing data platform

- Optimizing data pipeline performance and efficiency using techniques like partitioning, caching, compression, etc.

- Documenting data pipeline logic and assumptions using code comments or wiki pages

- Troubleshooting data pipeline failures and resolving data quality issues

Pipeline-focused data engineers are the ones who make sure that the data flows smoothly and correctly. They are like the plumbers, electricians, and mechanics of the data world. Without them, nothing would make sense.

Analytics-Focused Data Engineer

This type of data engineer is like a teacher. They help other people learn from the data and make decisions. Their responsibilities may include creating data models, setting up dashboards, and answering questions. They also provide feedback and suggestions to improve the usability and reliability of the data.

Analytics-focused data engineers need to be good at SQL, BI tools (such as Tableau or Power BI), and business logic. They also need to have strong analytical skills and a customer-centric mindset. They often act as a bridge between the technical and business sides of the organization.

Some examples of projects that analytics-focused data engineers might work on are:

- Designing and implementing a schema that supports various reporting needs

- Building a dashboard that tracks key metrics and trends

- Conducting root cause analysis on anomalies or outliers in the data

- Providing insights and recommendations based on data analysis

- Educating and training business users on how to use the data platform and tools

Analytics-focused data engineers are the ones who make sure that the data is useful and valuable. They are like the teachers, coaches, and consultants of the data world. Without them, nothing would matter.

Hire the right people.

As a recruitment consultant who is trying to simplify data engineering and help you hire the right people, it is important to understand these differences and match them with the appropriate candidates. The difficulty is Data engineering is not a one-size-fits-all discipline.