Analytics Engineer (m/f/d)

Allow us to introduce ourselves

Hello there! We’re Appinio, a global market research company that combines an AI-powered insights platform with expert consultancy. On a mission to make the world a little more fact-based, we help companies understand how consumers think and make smarter decisions.

We started out in Hamburg back in 2014, but we’re now humbled to call the world our playground, with over 260 employees spread across 13 countries (we’re remote-first), 2600+ international clients, delivering research from +190 markets, globally.

Want to be a part of the Appinio movement?

Here at Appinio, we value transparency in our recruitment process , and we've created a blog to share what to expect during each step of our journey together: here

Your mission as an Analytics Engineer

We are looking for an Analytics Engineer (m/f/d) with strong data engineering skills to join our Data team and help build a scalable, reliable, and well-structured data foundation across Appinio.
In this role, you will design and maintain clean, scalable data models that make data easy to access, understand, and use across the organisation. You will use SQL, dbt, and Python to transform raw data into meaningful datasets, develop automated workflows, and ensure consistency and reliability across our data warehouse.
Beyond data modelling, you will contribute to building, maintaining, and optimising data pipelines and integrations. You will work with APIs, databases, and third-party data sources to ensure reliable data ingestion, troubleshoot pipeline issues, and improve the overall stability and performance of our data infrastructure.
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 …