Data Analytics Engineer at Doodle

Analytics Engineer

Build the Data Foundation That Powers Every Product Decision

Doodle is a B2B SaaS platform used by millions of professionals to coordinate meetings and collaboration. As we continue expanding our platform, trusted data has become one of our most valuable products. Every product decision, customer insight, experiment, and business metric depends on reliable, well modelled data.

You are more than an Analytics Engineer. You are responsible for building the trusted data foundation that enables better decisions across Doodle. Working closely with Product, Engineering, Marketing, Finance, and Leadership, you will transform raw product data into trusted, scalable datasets that power experimentation, reporting, and product innovation.

What You Will Do

As an Analytics Engineer, you will own the analytics platform that enables self service insights across Doodle.

Analytics Platform

• Design, build, and maintain scalable analytics data models using dbt and modern data engineering practices.
• Develop reliable transformation pipelines that convert raw product data into trusted, business ready datasets.
• Own the performance, reliability, and continuous improvement of Doodle's analytics platform.
  
  

Data Modelling & Semantic Layer

• Define, document, and maintain trusted business metrics used across the company.
• Build and evolve a scalable semantic layer that enables consistent reporting and self service analytics.
• Ensure data definitions remain accurate, accessible, and aligned across teams.
  
  

Data Quality & Governance

• Own data quality, validation, testing, monitoring, and governance across the analytics stack.
• Implement best practices for documentation, version control, CI, and automated testing.
• Establish standards that improve data reliability, consistency, security, and trust.
  
  

Product Analytics & Experimentation

• Partner with Product Managers and Analysts to enable experimentation, funnel analysis, retention analysis, and customer insights.
• Support product launches by providing reliable measurement frameworks and trusted analytics.
• Enable accurate A B testing through high quality event modelling and instrumentation.
  
  

Cross Functional Collaboration

• Work closely with Product, Engineering, Data, Marketing, Finance, and Leadership teams.
• Translate business questions into scalable data models, trusted metrics, and actionable insights.
• Advise teams on analytics architecture, data modelling standards, and best practices.
• Influence product and business decisions by making data reliable, accessible, and easy to use.
  
  

Our Ideal Candidate

We are looking for an Analytics Engineer who combines strong engineering fundamentals with a product mindset and a passion for building trusted, scalable analytics platforms.

Analytics Engineering

• Strong experience building analytics data models using dbt or similar transformation frameworks.
• Experience orchestrating modern data pipelines using Airflow or similar workflow tools.
• Advanced SQL skills with experience working on large analytical datasets.
• Experience working with cloud data warehouses such as Amazon Redshift. Experience with Athena or Spark is an advantage.
  
  

Programming & Analytics

• Strong Python skills for data processing and automation.
• Good understanding of dimensional data modelling, database design, and analytics engineering principles.
• Familiarity with experimentation frameworks and product analytics.
  
  

Engineering Excellence

• Quality first mindset with experience in testing, documentation, version control, and CI.
• Passion for building reliable, scalable, and maintainable analytics platforms.
• Strong problem solving skills with excellent attention to detail.
  
  

Collaboration & Communication

• Excellent communication skills with the ability to explain technical concepts to both technical and non technical stakeholders.
• Comfortable working across Engineering, Product, Analytics, and business teams.
• Enjoy collaborating in a fast moving SaaS environment.
  
  

Nice to Have

• Experience with pandas or similar Python data processing libraries.
• Experience supporting Product Led Growth organizations.
• Experience building semantic layers or self service analytics platforms.
• Experience with product analytics tools such as Amplitude, Mixpanel, or GA4.
• Experience working in a modern B2B SaaS environment.
  
  

Hiring Journey

• Initial Application Review + BRYQ Assessment
• Hiring Manager Interview
• Technical Assessment
• Cross Functional Technical Interview
• Executive Interview + HR Interview
  
  

So, Get in Touch!

At Doodle, we are committed to providing an environment of mutual trust and respect, where equal employment opportunities are available to all applicants and teammates without regard to age, race, color, disability, religion, gender, or sexual orientation. Diversity and inclusion are important to us because the best products are built by teams with different experiences, perspectives, and backgrounds.

IMPORTANT NOTICE: Please note that we can only consider your application if you are based and have the right to work in Germany or London. At this time, we are unable to sponsor visas for this position or support relocation.