Data Analytics Engineer

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.

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