Building Data Teams 101: Perfect Blueprint for Scaling Mid-Sized Companies
Scaling your data team doesn’t have to be daunting—this guide breaks down the essential components for mid-sized businesses to develop a team that delivers insights and fuels growth.
Check my Leading Data Teams Playlist to learn more about how to build efficient data teams.
Greetings, curious reader,
You need more than hiring smart people to build a solid data team. Creating a team with clear roles and well-organised workflows makes all the difference.
Businesses with unstructured data teams often experience slower output, confusion, and missed deadlines. A well-organised team provides better collaboration, smoother workflows, and reliable data, which leads to better decision-making.
You and I understand that reliable data is essential for intelligent business decisions. When you structure your data team correctly, the data flow improves, leading to faster insights and better results.
Think of your data team as a machine. When every part knows its role, the machine runs smoothly. Your data team needs clear role definitions to avoid bottlenecks and ensure everyone does what they do best.
You know how people say every use case is different. We are not doing that. If you want to build a high-performing data team at a mid-sized product company, this practical guide is just for you.
Reading time: 11 minutes
What: Key Roles in a Structured Data Team
The foundation of a high-performing data team lies in having the right people in the right roles. Think about it: According to research by Effactory, companies see a 53% increase in efficiency and a 27% increase in effectiveness when they define roles and responsibilities clearly.
Each role has a specific focus, and together, they create a balanced team that drives faster insights, improves data quality, and keeps projects moving. Here’s a breakdown of the essential roles:
Role #1: Data Engineers: The Backbone of Your Infrastructure
Data engineers are the builders of your data infrastructure. Their primary responsibility is creating and maintaining the data pipelines that collect, transform, and move data across your organisation. They focus on ensuring data is accessible, clean, and reliable.
Core Responsibilities: Designing, building, and maintaining data pipelines. Automating data workflows. Ensuring data quality and availability.
Key Technologies: Tools like Apache Airflow, AWS Glue, and Docker help data engineers automate and manage pipelines efficiently.
Why is this role essential? Without solid data engineering, your entire data operation is at risk. When pipelines are slow or unstable, everything else breaks down. A robust data engineer ensures the rest of your team works with clean, accessible data. Data engineers enable faster analysis and better insights.
Role #2: Analytics Engineers: Translators Between Data and Insights
Analytics engineers sit between data engineers and analysts, ensuring that raw data is transformed into a usable format. They organise, clean, and structure the data so analysts can jump straight into delivering insights.
Core Responsibilities: Modelling and transforming data, building data marts, and ensuring analysts have access to clean, well-structured data.
Key Technologies: Tools like dbt and SQLMesh are a must for transforming raw data.
Why do you need them? Analytics engineers free analysts from the burdens of data prep. Without them, analysts spend more time fixing data than delivering valuable insights. By having this role, you speed up the data-to-insights process. That way, your team can deliver timely, actionable information to stakeholders.
Role #3: Domain-Specific Analysts: Experts in Actionable Insights
Domain-specific analysts specialise in delivering insights tailored to specific areas of your business—whether that’s marketing, sales, or product. They focus on making the data understandable and relevant to their department, driving decision-making based on reliable, targeted data.
Core Responsibilities: Analysing data to produce reports and insights for specific business areas. Communicating findings to non-technical stakeholders. Driving decisions in their domain.
Key Technologies: Tools like Looker, Sigma, and Power BI allow analysts to quickly visualise and share insights with stakeholders.
The benefit here is precision. By having analysts dedicated to specific domains, you ensure each part of your business gets relevant, focused insights. This reduces the noise and increases the impact of data-driven decisions.
Role #4: Data Scientists: For Advanced Analytics and Predictive Models
A data scientist is optional in this structure. While not every company needs one, data scientists bring advanced analytics capabilities. Think machine learning, predictive modelling, and statistical analysis. They focus on finding deeper insights through complex data processes.
Core Responsibilities: Building machine learning models, conducting statistical analysis, and applying advanced data techniques to solve complex problems.
Key Technologies: Tools like Python, Jupyter Notebooks, and Snowpark support data scientists in running advanced models.
A data scientist can add tremendous value if your business is ready to invest in predictive analytics or machine learning. However, before adding this role, it is crucial to ensure the rest of your data pipelines are in a solid state.