Data Analytics Engineer (For independent contractors)

Data Analytics Engineer II (GenAI Application Teams)

Role Overview

As a Data Analytics Engineer, you will design and develop scalable analytical solutions that support GenAI application teams across search & discovery, AI companions/chatbots, customer support AI applications, and LLM evaluation workflows.

This is a highly business-oriented role focused on bridging the gap between raw data and application-specific analytical needs. You will own data domains end-to-end, ensure data quality and usability, and transform complex datasets into actionable insights, monitoring systems, and evaluation frameworks.

This is not a traditional Data Engineer role. The focus is on application logic, analytics, data modeling, quality, experimentation, and insight generation - rather than infrastructure ownership or EL platform engineering.

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-serve 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 by impact, and drive initiatives end-to-end.

  
  

• Strong analytical thinking with the ability to derive insights beyond surface-level metrics.

  
  

• Experience contributing to the production environment, delivering timely and high-impact insights to various stakeholders.

  
  

• Experience in ML/AI product environments, evaluation workflows, or model/error analysis is a strong advantage.

  
  

• 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.

  
  

• Work extensively with free text, unstructured data, LLM-generated outputs, and AI application telemetry.

  
  

• Advantage: Experience with dbt, Snowflake, Streamlit, Airflow, or Argo.

  
  

• Advantage: Experience working with AI/LLM-related datasets and evaluation pipelines.

Collaboration & Communication

• Excellent communication skills with the ability to explain technical concepts to non-technical stakeholders.

  
  

• Proven ability to collaborate across product, engineering, analytics, and data science teams.

  
  

• Self-driven, proactive, and comfortable owning ambiguous problem spaces independently.