As an Analytics Engineer, you’ll collaborate with business stakeholders and feature teams to understand the data landscape of Customer Operations (Customer Service, After Sales, Retail). You’ll transform raw data into KPIs, clean tables, and insights through efficient data pipelines. Additionally, you'll proactively suggest data and analyses to support better decision-making.
What impact do you have?
You design, develop and manage efficient and clean datasets with coordinated business logic, which can be used for data-driven business decisions.
Partner with Customer Operations to understand data needs, translating business requirements into scalable data solutions. Collaborate on ad-hoc analyses and regular reports for impactful insights.
Develop and maintain dynamic dashboards and reports using tools like Tableau, SSRS, and SSAS. Ensure their relevance and effectiveness in supporting business monitoring, performance tracking, and optimization
Collaborate with feature teams to align on data requirements, integration, and progress, ensuring smooth communication and feedback between analytics and development teams.
Drive innovation by proposing and implementing advanced analytics solutions, enhancing insights for business partners, and supporting data-driven decisions across Customer Operations.
Collaborate with your team to continually refine and improve internal processes and tools, identifying opportunities for increased efficiency, automation, and scalability in analytics workflows.
What do you offer?
Completed degree (university / university of applied science / higher technical college)
in a technical or data-focused field.
1-3 years of practical experience
in a similar role
English (business fluent)
German (sound knowledge)
Higher education degree in a technical or data-focused field (ETH/UNI/FH) or relevant work experience is required.
1-3 years of practical experience in a similar role, excellent SQL, and good Python skills are essential (experience with BigQuery is highly advantageous).
Strengths include preparing, analyzing, and visualizing data, ideally with experience in Tableau, Reporting Services, and Multidimensional Models (Cubes).
Experience with sustainable data modeling and complex data pipeline design is important, with familiarity in using a data orchestration tool like dataform or dbt being a plus.
Affinity for processes and Business KPIs is essential, along with a willingness to visit colleagues in Customer Service and Returns departments to observe how reporting and analysis impact daily business.
Basic understanding of the steps involved in implementing Machine Learning algorithms is required.
Good German (at least B2) and English skills are mandatory.
Using creativity and initiative to solve problems and achieve goals.
Identifying opportunities for improvement and implementing changes to enhance productivity and effectiveness.
Prioritizing empathy and compassion in decision-making.