Data Analytics Engineer, Doodle

• 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
• 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
• 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
• 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
• 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

Desirable

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

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