Analytics Engineer

As an Analytics Engineer, you play a central role in building and maintaining the data models in our data platform (Core technologies: BigQuery, dbt).

You will develop key data models and data products for different business domains in close cooperation with analysts and engineers. You’ll help ensure that our data platform is best-in-class when it comes to data quality, and you’ll define and implement standards and best practices together with the team. You’ll also align with stakeholders and business analysts to define clear, consistent KPIs and metrics across the business.

You develop data models used in in reporting but also machine learning, system integration, personalization, and will build the data product for future AI use-cases

 
Your main responsibilities:

• Data Model development and optimization
  You develop data products on our Data Platform. Design and implement robust data models using dbt to transform raw data into clean, well defined, documented, and reusable datasets.

• Champion improvements in data modeling and quality
  Drive data quality, governance and standardization within the rest of the data team. Take a leading role in improving our data quality and usability. e.g. through standardization, automated testing, CI/CD pipelines enforcing standards.

Your team

This role is part of the Data & Analytics team, which includes Data Engineering, Tracking & Measurement, Data Science, Analytics Engineering, and Data Analysis. You will be part of a centralized data team that supports the entire organization, this means you will be working with a wide variety of data types and use cases.
 

What you will do

• Create long term value by focusing on creating solid fundamental data products with high quality, maintainability and reusability.
• Improve data quality through testing, documentation, and standardization, empowering analysts and scientists with greater autonomy.
• Facilitate optimal usability of data models, by training, documentation, and ensuring we keep in line with best practices.
• Collaborate with Data Analysts and Data Scientists to create efficient data models. And work together with Data Engineers to create solid foundational layers.
• Optimize data models and queries for performance and cost-efficiency.
• Support data governance, observability, and cataloging efforts.