Analytics EngineerNeu

Working closely with Analytics, Product, Engineering, and business teams, you will help define and standardise key business metrics such as revenue, bookings, and churn, ensuring a single source of truth across the company. You will also take ownership of data quality through automated testing, validation, and monitoring across the data lifecycle.

You will explore and leverage AI-powered tools to improve development workflows, automate repetitive tasks, and enhance data engineering and analytics processes. You will also help prepare our data foundation for AI-driven analytics by ensuring that data models, business definitions, and documentation are structured, consistent, and accessible.

You will contribute to documentation, performance optimisation, and the continuous improvement of our data stack, helping us build a robust and scalable data platform that enables teams across Appinio to make faster, more informed decisions.

What you’ll be doing

• Data Modeling: Design, build, and maintain clean, scalable data models in the data warehouse using SQL and dbt, making data accessible and reliable for analysts and business users.
• Data Transformation: Develop and maintain data transformation workflows, turning raw data from multiple sources into structured, analytics-ready datasets.
• Data Pipelines & Integration: Build, maintain, and troubleshoot data pipelines and integrations, ensuring reliable data ingestion from APIs, databases, and third-party systems into the data warehouse.
• Data Quality & Testing: Ensure data accuracy and reliability by implementing automated tests, validations, monitoring, and proactive issue resolution across data pipelines and models.
• Documentation: Document data models, pipelines, business definitions, and transformation logic to ensure transparency, maintainability, and knowledge sharing.
• Business Logic Ownership: Define, implement, and standardize key business metrics (e.g. revenue, bookings, churn) to ensure consistency across reporting and analytics.
• Collaboration: Work closely with analysts, business stakeholders, and engineering teams to understand data requirements and deliver reliable, scalable data solutions.
• Performance & Infrastructure Optimization: Optimize SQL queries, dbt models, and data pipelines for performance, scalability, and efficient use of data warehouse resources.

You will thrive in this role if

• Strong SQL & Data Modeling Skills: You have excellent SQL skills and hands-on experience designing, building, and maintaining scalable data models in a modern data warehouse.
• Experience with dbt: You have practical experience using dbt to develop, test, document, and maintain reliable data transformation workflows.
• Data Engineering & Python: You are proficient in Python and have experience building, maintaining, and troubleshooting data pipelines, including integrations with APIs, databases, and third-party systems.
• Data Warehousing Knowledge: You understand modern data warehouse architectures and have experience working with cloud-based solutions such as Amazon Redshift, Snowflake, or BigQuery.
• Data Quality & Reliability: You have a strong understanding of data quality principles, automated testing, validation, and monitoring, with a proactive approach to identifying and resolving issues.
• Business Understanding: You can translate business requirements into reliable data models and clearly defined metrics, balancing technical quality with practical business needs.
• Problem-Solving & Ownership: You are analytical, detail-oriented, and comfortable taking ownership of technical challenges, investigating root causes, and developing sustainable solutions.
• Collaboration & Communication: You communicate technical concepts clearly, work effectively with both technical and non-technical stakeholders, and enjoy collaborating across teams.
• AI & Automation Mindset: You are curious about AI-powered development tools and actively explore opportunities to automate repetitive tasks, improve workflows, and increase productivity.

What’s in it for you?

• Flexibility Policy - Average is 30 days taken, however, we do not have a hard cap of number of vacation days as we know life happens
• Temporary work from abroad - If you're based in the EU, you can work outside your country of residence for up to 180 days per year
• All the hardware you need and your own MacBook
• If you are located in Hamburg or Berlin, you'll get a Deutschland ticket or access to a mobility budget with the NAVIT app to get you to and from the office space
• Subsidised Urban Sports Club or ClassPass membership based on location
• In case you’re located in one of the cities where most of our fellow Appinioneers are (Hamburg, Berlin, Munich, London, Madrid, Barcelona, or New York), you can get access to our Co-working spaces

Additional role details

• This is a fully remote position open to candidates based in Germany, the UK, Portugal, the Netherlands, and Spain
• This role will report directly to our Team Lead, Business Intelligence

Our interview process

• Screening Call with the Talent Acquisition team
• Hiring Manager interview
• Case Study
• Executive Interview
• References

Kind reminder: Please ensure that your application is submitted in English.

This post not only outlines our ideal candidate but also serves as an inspiration for what this role can evolve into. Even if you consider yourself an 80% fit but have a genuine passion for this position, we encourage you to apply. We're eager to hear from individuals who share our enthusiasm.

Appinio is an equal-opportunity employer. All applicants will be considered for employment without attention to race, colour, religion, sex, sexual orientation, gender identity, national origin, veteran, or disability status.

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Learn more about us:

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Source: Statistisches Bundesamt (Destatis), table; dl-de/by-2-0; StarIn's calculation