Data Analytics Engineer II (GenAI Applications)
Netherlands | Hybrid
Full-time
Shape the Future of AI-Powered Products Through Data
We're looking for a highly analytical and business-oriented Data Analytics Engineer to join an innovative team building next-generation AI and GenAI products. This role sits at the intersection of analytics, product development, and data engineering, helping teams make smarter decisions through high-quality data, actionable insights, and scalable analytical solutions.
This is not a traditional Data Engineering position focused on infrastructure or platform development. Instead, you'll own analytical domains end-to-end, transforming complex datasets into insights that drive product improvements across AI-powered customer experiences.
What You'll Do
Own Data Domains End-to-End
- Design, develop, and maintain scalable analytical data models.
- Ensure data quality, accuracy, consistency, and reliability across critical datasets.
- Build reusable data products and analytical foundations for product teams.
- Support data governance, stewardship, classification, security, and compliance initiatives.
- Monitor and improve data pipeline health through troubleshooting, optimization, and proactive risk management.
Drive Insights & Product Decisions
- Turn large and complex datasets into actionable business insights.
- Build monitoring frameworks, operational dashboards, and quality reporting solutions.
- Analyze product experiments, user behavior, AI model performance, and evaluation outcomes.
- Perform exploratory analysis to validate new product features before launch.
- Partner closely with Product Managers, Engineers, Data Scientists, and Analytics stakeholders to identify opportunities and drive data-informed decisions.
Support AI & GenAI Applications
You'll work with data generated by cutting-edge AI products, including:
- Conversational AI and chatbot applications
- Search and discovery experiences
- Customer support automation solutions
- Large Language Model (LLM) evaluation workflows
- AI application telemetry and performance analytics
What We're Looking For
Experience
- 3+ years of experience in Analytics Engineering, Data Analytics, Product Analytics, or other highly analytical data-focused roles.
- Strong business and product mindset with the ability to connect data insights to real-world outcomes.
- Experience owning analytical problems from definition through delivery.
- Proven ability to work independently in ambiguous environments and prioritize based on business impact.
- Experience delivering insights to stakeholders and contributing to production environments.
Technical Skills
- Advanced SQL skills with experience working in large-scale analytical environments.
- Strong Python experience, including data processing and analysis.
- Hands-on experience with PySpark.
- Strong understanding of data modeling and modern data warehouse concepts.
- Experience building scalable, maintainable analytical solutions and transformations.
- Comfortable working with structured, semi-structured, and unstructured data, including AI-generated content and text-based datasets.
Nice to Have
- Experience with dbt, Snowflake, Airflow, Argo, or Streamlit.
- Exposure to AI, machine learning, LLM products, evaluation frameworks, or model performance analysis.
- Experience analyzing experimentation results and product performance metrics.
Personal Attributes
- Strong analytical and critical thinking skills.
- Excellent stakeholder management and communication abilities.
- Curious, proactive, and solution-oriented.
- Comfortable translating technical findings into business recommendations.
- Passionate about using data to solve complex product challenges.
Why This Opportunity?
- Work on some of the most exciting AI and GenAI products in the market.
- Own meaningful analytical domains and influence product strategy.
- Collaborate with multidisciplinary teams including Product, Data Science, Engineering, and AI experts.
- Solve complex business problems using data at scale.
- Gain exposure to cutting-edge LLM and AI evaluation workflows.