Freelance Data Analytics Engineer - GenAI

We are seeking a Data Analytics Engineer to support our GenAI Application Teams, working across areas such as search & discovery, AI companions/chatbots, customer support systems, and LLM evaluation workflows. This role is highly business-oriented, focusing on bridging the gap between raw data and real-world application needs. You will own data domains end-to-end, ensuring data quality, usability, and impact by transforming complex datasets into actionable insights, monitoring systems, and evaluation frameworks.

⚠️ This is not a traditional Data Engineering role — the emphasis is on analytics, data modeling, experimentation, and insights, rather than infrastructure or platform ownership.

Key Responsibilities

Data Modeling & Ownership

• Own business and application data domains end-to-end
• Ensure data accuracy, consistency, and quality across datasets and logging
• Design and maintain scalable data models and reusable analytical datasets
• Drive data governance: classification, stewardship, quality monitoring, security, and compliance
• Monitor and improve pipeline health through troubleshooting, performance tuning, and proactive risk mitigation

Analytics, Monitoring & Insights

• Transform large, complex datasets into actionable business insights (operational, historical, predictive)
• Build reusable datasets enabling self-service analytics across teams
• Develop monitoring tables, quality dashboards, and cost dashboards for GenAI applications
• Analyze experiments, model behavior, and LLM evaluation results
• Validate GenAI products through exploratory analysis and visualization prior to release
• Collaborate with product managers, ML scientists, and engineers to define analytics roadmaps and uncover opportunities

Qualifications & Skills

• 3+ years of experience in analytics, data, or software-related roles working with large-scale data systems
• Strong business mindset with a focus on delivering customer value
• Ability to navigate ambiguity, prioritize effectively, and drive initiatives end-to-end
• Strong analytical thinking with the ability to uncover deep insights beyond surface metrics
• Experience contributing to production environments and delivering high-impact insights to stakeholders
• Exposure to ML/AI product environments, evaluation workflows, or model analysis is a strong advantage
• Resourceful in leveraging AI tools and assistants to improve productivity and outcomes

Technical Requirements

• Strong SQL skills and experience with analytical databases
• Hands-on experience with Python and PySpark
• Proven experience in data modeling and modern data warehousing practices
• Ability to build maintainable, reusable, production-grade analytical pipelines and transformations
• Experience working with unstructured data, free text, LLM outputs, and AI telemetry

Nice to Have:

• Experience with dbt, Snowflake, Streamlit, Airflow, or Argo
• Familiarity with LLM datasets, GenAI systems, or evaluation pipelines