Common Data Team Structures (Engineer vs Analyst vs Scientist)

Summary

This video provides a high-level overview of common data team structures, emphasizing roles across data engineering, analysis, and science. It introduces the 'three pillars of data engineering': product teams (owners of source systems), data engineers (responsible for building pipelines and integrations), and data analysts (who bridge technical and business needs). The video also explores newer hybrid roles, such as analytics engineers, who combine aspects of data engineering and analysis. Lastly, it distinguishes data scientists, who focus on predictive analytics and machine learning, from other data roles. The goal is to help viewers understand team dynamics and design efficient data operations.

Takeaways

• 😀 Data teams consist of multiple roles beyond just data engineering, including product teams, analysts, and data scientists.
• 😀 Product teams are responsible for owning source systems like Salesforce, QuickBooks, or internal databases, ensuring data is collected before reaching the central hub.
• 😀 Data engineers focus on backend operations such as data pipelines, integrations, and transformation to central hubs like data lakes or warehouses.
• 😀 Data analysts act as a bridge between technical teams and business stakeholders, ensuring that the data provided meets the business needs.
• 😀 The analytics engineer role is a hybrid position that blends data engineering and data analysis, often using tools like DBT to create custom data models.
• 😀 The data scientist focuses on advanced analytics like machine learning, statistical modeling, and predictive analysis, relying on the data infrastructure set by data engineers.
• 😀 Data architecture is often represented by three main pillars: Sources (data origin), Central Hub (data warehouse/lake), and Insights (data visualization, reporting, and machine learning).
• 😀 Data engineers collaborate with product teams and software developers to ensure the data being collected is correct and logically structured for downstream use.
• 😀 Data analysts use tools like DBT, write queries, and create dashboards to turn raw data into actionable insights for business stakeholders.
• 😀 The role of the analytics engineer is becoming increasingly important as it fills the gap between backend engineering and user-facing analysis, making it a critical part of modern data teams.

Q & A

What are the main components of a data engineering team as described in the video?

The main components of a data engineering team include the product team (owners of source systems), data engineers (handling the backend integrations and pipelines), data analysts (bridging the technical and business sides), analytics engineers (a hybrid role between data engineers and analysts), and data scientists (focusing on predictive and machine learning analytics).

How does the product team fit into the data team structure?

The product team is responsible for the source systems that feed data into the central hub. This can include tools like Salesforce, QuickBooks, Google Ads, or internal databases. The product team ensures the data from these systems is available for processing by the data engineering team.

What role does a data engineer play in a data team?

A data engineer is responsible for integrating data from source systems into a central data hub, creating data pipelines, transforming data, and automating processes to ensure data flows smoothly into a data warehouse or lake for analysis.

What is the role of a data analyst in a data engineering team?

The data analyst acts as a bridge between technical teams and business stakeholders, ensuring that data delivered from the central hub meets business needs. They create reports, dashboards, write queries, and often use tools like DBT to generate custom data models for analysis.

What is an analytics engineer, and how does their role differ from that of a data engineer?

An analytics engineer is a hybrid role that combines aspects of both data engineering and data analysis. They work on customizing data models for business use and may work with tools like DBT to automate tasks, whereas data engineers focus more on backend integration and data pipeline construction.

How do data engineers and analytics engineers collaborate within a team?

Data engineers and analytics engineers work closely together, with the data engineer focusing on backend integrations, scheduling, and building the core data model, while the analytics engineer works on customizing that model for business needs and user-facing purposes.

How does the role of a data scientist differ from that of a data engineer?

A data scientist focuses on advanced analytics, including predictive modeling, machine learning, and statistical analysis. They work with structured data provided by the data engineer to build algorithms and derive deeper insights, whereas data engineers focus more on data infrastructure and integration.

What tools or skills are commonly used by data analysts and analytics engineers?

Data analysts and analytics engineers commonly use tools like SQL for querying data, DBT for automating data transformation, and business intelligence tools like Tableau or Power BI for creating reports and dashboards. Analytics engineers may also have advanced skills in data modeling and automation.

Why is the role of an analytics engineer becoming more important in modern data teams?

The role of an analytics engineer is becoming more important due to the rise of automation tools like DBT, which make it easier for people to work with data models. This hybrid role helps bridge the gap between data engineering and data analysis, allowing for more efficient handling of data and customization for business needs.

What should companies consider when structuring their data teams?

Companies should consider how to align roles in a way that promotes collaboration between technical and business-focused positions. They need to ensure that data engineers, analysts, and scientists can work together efficiently while clearly defining the responsibilities of each role based on the company’s needs.