Data Analytics Engineer (GenAI Application Team)
Summary
Analytics engineer owning analytical data domains end-to-end for GenAI applications: building scalable data models, reusable data products, dashboards, and LLM evaluation frameworks to drive data-informed decisions. Core stack is SQL, Python, and PySpark with modern data-warehouse practices.
In this role you translate raw data into actionable analytics for GenAI application teams, focusing on analytics, data modeling, quality, and insights rather than building data infrastructure. You own analytical data domains end-to-end and develop reusable data products, dashboards, and evaluation frameworks. You will partner with product, research, and engineering to drive data-informed decisions across AI-powered applications. This is an impact-focused, product-minded analytics role within a GenAI environment.
Verantwoordelijkheden- Own business and application data domains end-to-end, ensuring data quality and logging accuracy
- Design and maintain scalable analytical data models and reusable data products
- Conduct data governance tasks including quality monitoring, compliance, and security considerations
- Maintain data pipeline health through monitoring, troubleshooting, and performance tuning
- Transform large datasets into actionable insights for operational, historical, and predictive analysis
- Build self-service analytics datasets and monitoring/dashboards for quality and cost metrics
- Analyze experiments and LLM evaluation results; validate data and GenAI products via exploration and visualization
- Collaborate with product managers, scientists, and engineers to define analytics roadmaps
- 3+ years in analytics, data, or software-adjacent roles with large-scale data
- Strong business orientation and customer-focused mindset
- Ability to navigate ambiguity and drive end-to-end initiatives
- Proven ability to derive meaningful insights and support production environments
- Experience with ML/AI product environments or evaluation workflows is a plus
- Resourceful use of AI tools and assistants
- Strong SQL skills in analytical environments
- Hands-on Python and PySpark experience
- Data modeling expertise and modern Data Warehouse practices
- Experience with unstructured data, LLM outputs, and AI telemetry
- excellent communication
- cross-functional collaboration
- self-driven and proactive problem-solving
- SQL
- Python
- PySpark
