Building Cleo

Analytics Engineering at Cleo

Learn more about our Analytics Engineering team. They do some cool stuff.

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Our Analytics Engineers are an awesome team of people who champion data analysis at Cleo with determination, resourcefulness, and a contagious sense of humour.

Want to hear more? You’re in the right place.

Andrea, Analytics Engineer Lead, explains why the AE team exists and how they enable our squads to deliver greater value to our users.

We believe that high quality data is the foundation of empowered decision making

Our Vision: Everyone at Cleo can instantly access the information they need to build better products faster.

Our Mission: Ensure equal access to rich, accurate, and reliable data that drives impactful analysis and fast decision making

Our Promise: Good quality data, on time, every day

We value these three things over everything else

  • Collaboration. We work closely with people across Cleo to find the best solutions together, because together is best, right?
  • Innovation. We’re always looking to improve our processes, tools, and technologies to support Cleo’s success.
  • Continuous learning. We never stop learning and applying new techniques and technologies to our work.

What do we do?

We collaborate with everyone at Cleo, including product analysts, data scientists, engineers, product managers, designers, user researchers, copywriters, and leadership. Together, we define clear requirements, establish timelines, set expectations, and deliver value — all while seeking feedback to ensure our colleagues' data needs are met.

What do people need from data at Cleo?

  1. A single source of data. A centralised, reliable, well-documented and consistent source of data to make the right decisions for our users.
  2. High-quality insights. Our squads need actionable insights that are relevant, accurate, and timely. Product Analysts need high-quality data models to discover better product insights.
  3. High-velocity in analysis. Fast and easy access to insights to respond quickly to changing business needs.
  4. A great analytical experience. Intuitive and user-friendly analytical experience that allows people to explore data, see important information visually, and collaborate with others.

How do we meet these needs?

  1. Landing new data. We work with product analysts, product managers, and data engineers to ingest new data into Redshift, our data warehouse service.
  2. Building new data models. We build high-quality data models that are scalable, efficient, and reliable. We focus on building robust data pipelines, designing an efficient data warehouse, and developing analytics solutions that give actionable insights to our squads.
  3. Monitoring data quality. We ensure the quality of the data we ingest, transform and load into our data warehouse. We do this by setting up automated processes that check for consistency, completeness, and accuracy. We also proactively identify and fix data quality issues to maintain the reliability of our analytics.
  4. Supporting Product Analysts. We work closely with product analysts to understand their data needs and help them access the data they require for their analysis. We also assist with data modelling and SQL queries to help them extract insights efficiently.
  5. Empowering self-service. We support product analysts in the creation of self-serve analytical solutions.
  6. Writing Documentation. We make sure all our data models have exhaustive and clear documentation that can be easily accessed.

What does the next 12 months look like?

In the next year, we will focus on 3 main areas to improve our ways of working and stay up to date with the latest trends.

Anomaly Detection

Detecting anomalies early is crucial to ensuring the reliability and accuracy of our data. We’re looking into using a variety of techniques like statistical modeling and machine learning to spot anomalies in the data and the metrics we calculate.

Data Ingestion

We work with Data Engineers to define a data ingestion framework that will enable analytics engineers to ingest data in an efficient and standard way. This will help reduce the current time to insight, meaning we can deliver value to our users quicker than ever.

Integrate LLMs to boost data democratisation

We aim to improve the accessibility and democratisation of data by integrating LLMs with our tech stack. This will help people at Cleo to access data more easily and efficiently.

How will we measure success?

We’ll be measuring success by the impact of our solutions on Cleo, as well as the feedback from our colleagues.

In terms of metrics, we’ll look to track the accuracy and reliability of our data, the speed and efficiency of our data pipelines and analytics solutions, and the satisfaction of our colleagues with the support and services we provide.

Sound interesting?

We’re looking for Analytics Engineers and Data Scientists to join our team.

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