The Rise of the Analytics Engineer – and common signs you might need one

In the last five years, the role of the analytics engineer has risen to prominence, particularly in technology-centred and data-driven organizations. The role of the analytics engineer is often closely associated with the emergence of dbt, a powerful data transformation tool developed by Fishtown Analytics (now dbt Labs), which has been instrumental in defining and popularizing this role within data-driven organizations. 

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Still, on a global scale and when zooming out from the tech world, the analytics engineer remains far less known than the more familiar data-oriented roles; data analyst, data engineer, and data scientist (see Google search trends below). As companies increasingly rely on data to drive decision-making and strategic initiatives, we also find they mostly recruit the three latter roles in the process. While a company can do great with data analysts and/or -scientists supported by a few data engineers, especially early in its journey, onboarding analytics engineers can help scale the impact of the other roles as the data and complexity grows.

In this post, we explore when it makes sense for organizations to incorporate analytics engineers and outline the key focus areas for this role.

The Evolution of the Analytics Engineer Role

While data analysts typically focus on interpreting data, and data engineers concentrate on building data pipelines, analytics engineers sit at the intersection of these roles, bridging the gap between raw data and actionable insights. They are responsible for transforming data into a structured, reusable form, ensuring that all teams in an organization are working from a single source of truth. Despite its growing prevalence, the advantages of the analytics engineer role is still less understood, and the responsibility for data transformation might fall under either of the other data roles (or worst case: in several of them simultaneously).

Now more than ever, with the growing amount of data, there is an increased need for neater task distribution. The analytics engineer takes ownership of creating reliable data models, and supports the diverse needs of different data consumers.

When Does It Make Sense to Have Analytics Engineers?

Analytics engineers become crucial in organizations that have already established a working data platform where data is reliably ingested and stored. However, despite having a solid data foundation, these organizations may struggle with multiple versions of the truth. In such environments, analysts often spend significant time building and rebuilding their own business logic, leading to inefficiencies and inconsistent reporting.

If your organization is facing these challenges, it might be time to consider hiring analytics engineers. Their expertise in standardizing and structuring data can help ensure consistency and accuracy, enabling analysts to focus more on generating insights and less on data preparation. 

The analytics engineer will be most effective when your organization has a clearly defined metrics framework and a north star metric. If this is not the case, the Analytics Engineer could contribute to set these prerequisites.

Key Initial Focus Areas for Analytics Engineers

Once an organization decides to bring in analytics engineers, it’s essential to prioritize their efforts to maximize impact. Here are the key areas where analytics engineers should focus:

1. Break Down Metrics into Components

After defining the North Star metric, the next step is to break it down into its core components. This process involves mapping out how different parts of the business contribute to the overall goal. For instance, if profit is the North Star, analytics engineers might break it down into revenue and cost, with further subcategories for different revenue streams and cost drivers.

This breakdown helps in identifying which areas need improvement and where the organization is excelling, providing a clear roadmap for data-driven decision-making.

2. Conduct a Technical Evaluation and Map Out Data Sources

Once the metrics are established, analytics engineers should perform a technical evaluation to map out where the source data resides. This step involves identifying the "single source of truth" for each data point, collaborating with data engineers and potentially software engineers in ensuring that all data models are built on reliable, accurate data.

By understanding the data landscape, analytics engineers can prevent discrepancies and ensure that everyone in the organization is working from the same dataset, reducing the risk of conflicting reports and analyses.

3. Map Out Core Concepts and Build Data Models

With a clear understanding of the metrics and data sources, the next focus area is to map out the core concepts within the organization and build corresponding data models. These models are essential for transforming raw data into structured, meaningful information that can be easily used by analysts and other stakeholders.

Building robust data models ensures that complex business logic is embedded within the data infrastructure, allowing for more consistent and reliable reporting across the organization. This is where dbt shines as a tool, allowing complex data models to be generated with minimal code repetition, built-in documentation, tests and lineage.

As you develop these models, prioritize your efforts based on high-impact use cases. Instead of attempting to cover every area at once, focus on building scalable, end-to-end solutions. A solid conceptual data model will serve as a roadmap, preventing the need for extensive rebuilding as you tackle each case.

4. Next steps: Consider a Semantic Layer

For organizations with more mature data needs, implementing a semantic layer might be the next step. A semantic layer acts as an abstraction layer that allows non-technical users to interact with data without needing to understand its underlying complexity. This layer can further enhance consistency and accessibility, making it easier for different teams to extract the insights they need without having to reinvent the wheel. Stay tuned for a blog post discussing the Semantic Layer specifically. 

Building the Analytics Engineering Team

Building an effective analytics engineering team requires careful consideration of your organization’s specific needs and challenges. Here are a few key considerations:

• Skills and Experience: Look for candidates with a strong background in data modelling, SQL, and business intelligence tools. Experience in building and maintaining data warehouses is also crucial, as is the ability to collaborate with both technical and non-technical stakeholders.
• Collaboration and Alignment: Ensure that the analytics engineering team is closely aligned with both data engineering and data analysis teams. Regular communication and collaboration are essential to ensure that everyone is working towards the same goals and that the data models and metrics remain consistent.
• Scalability: As your organization grows, the demands on your data infrastructure will increase. Analytics engineers should be able to build scalable solutions that can handle increasing volumes of data and more complex business logic.

Conclusion: The Strategic Value of Analytics Engineers

The role of the analytics engineer is becoming increasingly critical as organizations strive to become more data-driven. By focusing on standardizing metrics, building robust data models, and ensuring consistency across the organization, analytics engineers can help unlock the full potential of your data.

If your organization is grappling with multiple versions of the truth or long lead time to make data available, it may be time to invest in an analytics engineering team. Their expertise can streamline data workflows, enhance collaboration, and ultimately drive more informed, data-driven decision-making across the organization.