Optimising the Performance of Analytics Engineers

Maximize the potential of your analytics team with novel approach for optimizing the performance of analytics engineers.
Optimising the Performance of Analytics Engineers

Too Long; Didn’t Read (TLDR)

Analytics Engineers provide the data needed to inform decisions and strategies. As the complexity of data increases, so does the need to optimise their performance to maintain the accuracy and reliability of results.

This blog post outlines the critical elements for successful analytics engineering performance optimisation, including leveraging existing tools and technologies, creating well-defined tasks with clear objectives, providing resources to ensure success, and ongoing feedback mechanisms.

Additionally, businesses should empower analytics engineers to make decisions within agreed limits and be encouraged to create an atmosphere of team collaboration. By following these best practices, organisations can ensure that their analytics team operates optimally.


In today's business landscape, data is no longer just an afterthought but an essential puzzle piece. As businesses increasingly rely on data to drive decisions, this proves the importance of analytics engineering.

This critical process involves the creation of robust data pipelines that transform raw data into meaningful insights. Data can be challenging to access without an analytics practice, unreliable, and ineffective in driving informed decision-making. A company that invests in high-quality analytics practices lays the foundation for long-term success.

The Hierarchy of Needs Framework for Analytics Engineers provides businesses with a structured approach to establish a strong foundation for their analytics practice. The framework consists of three phases: the Need to Survive, the Need to Succeed, and the Need to Grow. The first phase focuses on establishing core functionality and aligning analytics practices with business objectives. The second phase optimises the effectiveness of analytics practices, and the third phase focuses on developing advanced capabilities to improve performance constantly.

The framework helps businesses confidently navigate the continually changing data landscape, create an analytics infrastructure that can quickly grow with their company, and ultimately drive growth and success.

Need to Survive

The Need to Survive phase helps businesses establish the foundational analytics capabilities to leverage data effectively. These include building a secure and reliable infrastructure, understanding and documenting business rules, establishing governance processes, and collecting the correct data to meet business objectives. Most of the work an Analytics Engineer will complete is documenting, testing and building data transformations.

Secure and reliable infrastructure

The modern cloud-based data warehouse is an invaluable asset for businesses of all sizes. It provides the ability to store and analyse massive volumes of data securely and cost-effectively. By leveraging the power of the cloud, companies can quickly access their data from any location and share it with customers, partners, and other stakeholders. The scalability of these solutions allows organisations to grow as their needs change, eliminating the need for costly hardware investments.

Additionally, cloud-based data warehouses provide advanced analytics capabilities that enable users to gain deeper insights into their operations and help them make decisions faster.

Moving away from ETL to an ELT-based method of moving data gives merit to this. It has become easy to transfer data from one point to another using over-the-shelf tools like Fivetran, Airbyte or Stitch Data.

Understanding and documenting business rules

A group of folks believe that the responsibility to get the underlying business rules differs from that of the Analytics Engineer. They think that it should be the responsibility of a subject matter expert.

We have an alternative to present:  The Analytics Engineer should be the one to document and understand the data, while subject matter experts can provide additional context and sign off the documentation.

This approach helps ensure that all your data is properly documented, especially in an environment where the Analytics Engineer is face-to-face at the coal face daily.

A problem still exists, however: Analytics Engineers are, by definition, not Business Analysts. It is becoming more prevalent today where the Analytics Engineer has to have context (and access to documentation) related to the interdependencies and nuances of each dataset and data transformation. The line between analytics and business analysis is becoming blurred, so the responsibility for understanding data becomes increasingly important.

We still need to ponder some open questions: How do you ensure that the Analytics Engineer can document data at this level? How do you ensure the subject matter expert has the necessary context and can adequately sign off on the documentation?

We continue to explore these questions, but it is clear that, at minimum, the Analytics Engineer must understand and document critical information about their datasets. It is one of the fundamental steps for survival.

Ensuring data quality and data integrity

Regarding data analysis, the adage "garbage in, garbage out" holds. If you rush into analysis without a solid expectation of high-quality data, the results will be meaningless, potentially damaging your business and your team's reputation. Reputational damage is a secondary thought when organisations rush into data projects and forget to consider the importance of data quality.

Proactively safeguarding your team from data-quality risks is easy with the right tools and strategies. Having customised guardrails in place can give everyone peace of mind that any new, unexpected changes in datasets won't derail progress—and when issues arise, quick identification means resolutions arrive even faster. All of this without upsetting the CEO with inconsistent numbers later down the line destroying data trust.

To ensure the integrity of your production data, replicating it in a non-production environment is incredibly important. It allows you to evaluate the quality and run queries without fear of affecting critical systems - ensuring that potential risks are avoided.

At this point, the data team can start asking fundamental questions about data structures and relationships in each source system and develop an understanding of the nuances of the datasets while documenting. You will be setting yourself up for failure if you rush into doing analyses before you have a solid account of the quality of the data and the rules governing it.

Need to Succeed

The outputs of the team start to become tangible here to external stakeholders. The work up to this point is dedicated to making sure that the business, as a whole, speaks a common language and that you will keep production up when you run expensive queries. It also includes the added benefit of having quality data.

The departure point here is to codify the business rules into data transformations and, most importantly, visualise the outputs.

Data Modelling

There are multiple methods to transform and model data; we are biased in that we prefer data build tool (dbt) for this. The objective is to create a data layer that enables businesses to answer questions and ultimately build insights.

It is a long, arduous process, but when done right with leveraging the documentation process completed prior and the mutual understanding of a common language when it comes to data in the business, it will yield considerable benefits in terms of return on investment and agility.

Data Visualisation

It is worth mentioning that once you start to include stakeholders into the fold by showing them visualisations, the trust in the ability of the team to execute becomes significantly more prominent.

It is about ensuring that you have a performant data model and a data model that will advance your business by providing valuable insights.

Your data is only as good as it is helpful to stakeholders.

Need to Grow

The third and final phase combines the previous two into initiatives of advanced analytics techniques and moving towards prescriptive analytics. As businesses strive to stay ahead, advanced analytics techniques are becoming increasingly important in decision-making.

This process's third and final phase involves combining the insights from earlier stages to develop initiatives that use these techniques. From dashboards that provide real-time data to machine learning algorithms that gather vast amounts of data on customer behaviour, businesses can leverage their data to drive better results.

By moving towards prescriptive and predictive analytics, businesses can better understand why specific outcomes occur and take proactive measures to optimise their strategies.

By combining data-driven insights with informed decision-making, companies can achieve a competitive advantage within their industry, fostering growth and success.


Analytics practices empower businesses to make informed decisions and achieve growth. The Hierarchy of Needs Framework is a novel approach when building a robust analytics program.

By systematically tackling the three phases, organisations can establish a reliable foundation for data-backed decision-making, optimise effectiveness and engineering performance, and unlock advanced capabilities to improve performance continually.

When utilised correctly, data analytics enables companies to understand their business better and will allow them to thrive in today’s competitive environment. Investing in high-quality analytics practices is critical for long-term success, as it gives organisations insight into how they can perform better in various aspects of their businesses. As such, investing time and resources into robust analytics practices provides companies with opportunities to differentiate themselves from competitors and significantly increase profits.

Horizon Data is a full-stack data analytics consultancy. We are happy to engage further.

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