Data Analytics Engineer II (GenAI Application Teams)

Role Overview

Important Hiring Notes

• Looking for candidates with a stronger analytics and product mindset than a traditional Data Engineering profile.
• Former Senior Data Engineers or Data Architects focused primarily on infrastructure and platform development are unlikely to be a strong fit.
• The role is not centered on building data infrastructure from scratch.
• Focus areas include deriving insights, defining meaningful metrics, solving business problems through data, and processing large datasets in a scalable manner.
• Ideal candidates have owned analytical domains end-to-end, including:
  • Data modeling
  • Data quality
  • Experimentation analysis
  • Dashboarding
  • Monitoring
  • Stakeholder-facing analytics
• Target profile:
  • Approximately 2/3 analytics, product thinking, and business problem-solving
  • Approximately 1/3 data engineering and data modeling
• Candidates will be assessed on both technical data modeling skills and business orientation.

Responsibilities

Data Modeling & Ownership

• Own business and application data domains end-to-end.
• Ensure correctness, quality, and consistency of analytical datasets and logging.
• Design and maintain scalable analytical data models and reusable data products.
• Perform data governance responsibilities, including data classification, stewardship, quality monitoring, compliance, and security considerations.
• Maintain and improve data pipeline health through monitoring, troubleshooting, performance tuning, and proactive risk mitigation.

Analytics, Monitoring & Insights

• Transform large and complex datasets into actionable insights for operational, historical, and predictive analysis.
• Build reusable analytical datasets enabling self-service analytics across teams.
• Develop application-specific monitoring tables, quality dashboards, and cost dashboards.
• Analyze experiments, model behavior, and LLM evaluation results.
• Validate data and GenAI products through exploratory analysis and visualizations before release.
• Partner with product managers, scientists, and engineers to identify analytical opportunities and define analytics roadmaps.

Qualifications & Skills

Experience & Mindset

• 3+ years of experience in analytics, data, or software-adjacent roles working with large-scale data systems.
• Strong business orientation and customer-focused mindset.
• Ability to independently navigate ambiguity, prioritize based on impact, and drive initiatives end-to-end.
• Strong analytical thinking and ability to derive meaningful insights.
• Experience supporting production environments and delivering high-impact insights.
• Experience in ML/AI product environments, evaluation workflows, or model/error analysis is a strong plus.
• Resourceful and thoughtful use of AI tools and assistants.

Technical Skills

• Strong SQL skills and experience working with relational databases in analytical environments.
• Hands-on experience with Python and PySpark.
• Strong experience with data modeling and modern Data Warehouse practices.
• Experience building maintainable, reusable, production-grade analytical code and transformations.
• Experience working with free text, unstructured data, LLM-generated outputs, and AI application telemetry.
• Nice to have:
  • dbt
  • Snowflake
  • Streamlit
  • Airflow
  • Argo
• Nice to have: AI/LLM-related datasets and evaluation pipelines experience.

Collaboration & Communication

• Excellent communication skills.
• Ability to explain technical concepts to non-technical stakeholders.
• Proven cross-functional collaboration with product, engineering, analytics, and data science teams.
• Self-driven and comfortable owning ambiguous problem spaces.