• Analyze large-scale datasets related to agent performance, support interactions and QA. • Write optimized SQL queries using tools like BigQuery and ClickHouse. • Build dashboards in Tableau, Metabase. • Detect behavioral and performance trends among agents and support teams. • Partner with QA to identify root causes in agent behavior and customer issues. • Use LLM tools with practical prompt engineering for daily analytical tasks, summarization, and insights generation. • Ability to assess and profile LLM outputs for quality, relevance, and analytical correctness in business contexts. • Familiarity with LLM APIs and integrating model outputs into analytical or internal tooling workflows. • Work with Airflow and Python to automate data tasks and support workflows. • Optimize analytics pipelines and data marts to improve performance, resource efficiency, and reliability. • Identify and refactor inefficient DAGs, queries, and transformations to ensure scalable and cost-effective data processing. • Suggest and co-build improvements in agent tooling, ticket routing, and SLA tracking. • Translate business inefficiencies into trackable metrics and measurable outcomes. • Act as an analytical partner to Operations, Support Management, QA, and Training. • Work with Data Engineering to ensure clean, accurate, and well-modeled data pipelines. • Communicate findings clearly through presentations, visualizations, and concise documentation.

• Analyze large-scale datasets related to agent performance, support interactions and QA. • Write optimized SQL queries using tools like BigQuery and ClickHouse. • Build dashboards in Tableau, Metabase. • Detect behavioral and performance trends among agents and support teams. • Partner with QA to identify root causes in agent behavior and customer issues. • Use LLM tools with practical prompt engineering for daily analytical tasks, summarization, and insights generation. • Ability to assess and profile LLM outputs for quality, relevance, and analytical correctness in business contexts. • Familiarity with LLM APIs and integrating model outputs into analytical or internal tooling workflows. • Work with Airflow and Python to automate data tasks and support workflows. • Optimize analytics pipelines and data marts to improve performance, resource efficiency, and reliability. • Identify and refactor inefficient DAGs, queries, and transformations to ensure scalable and cost-effective data processing. • Suggest and co-build improvements in agent tooling, ticket routing, and SLA tracking. • Translate business inefficiencies into trackable metrics and measurable outcomes. • Act as an analytical partner to Operations, Support Management, QA, and Training. • Work with Data Engineering to ensure clean, accurate, and well-modeled data pipelines. • Communicate findings clearly through presentations, visualizations, and concise documentation.

• Analyze large-scale datasets related to agent performance, support interactions and QA. • Write optimized SQL queries using tools like BigQuery and ClickHouse. • Build dashboards in Tableau, Metabase. • Detect behavioral and performance trends among agents and support teams. • Partner with QA to identify root causes in agent behavior and customer issues. • Use LLM tools with practical prompt engineering for daily analytical tasks, summarization, and insights generation. • Ability to assess and profile LLM outputs for quality, relevance, and analytical correctness in business contexts. • Familiarity with LLM APIs and integrating model outputs into analytical or internal tooling workflows. • Work with Airflow and Python to automate data tasks and support workflows. • Optimize analytics pipelines and data marts to improve performance, resource efficiency, and reliability. • Identify and refactor inefficient DAGs, queries, and transformations to ensure scalable and cost-effective data processing. • Suggest and co-build improvements in agent tooling, ticket routing, and SLA tracking. • Translate business inefficiencies into trackable metrics and measurable outcomes. • Act as an analytical partner to Operations, Support Management, QA, and Training. • Work with Data Engineering to ensure clean, accurate, and well-modeled data pipelines. • Communicate findings clearly through presentations, visualizations, and concise documentation.

- Analyze large-scale datasets related to agent performance, support interactions and QA. - Write optimized SQL queries using tools like **BigQuery** and **ClickHouse**. - Build dashboards in **Tableau**, **Metabase**. - Detect behavioral and performance trends among agents and support teams. - Partner with QA to identify root causes in agent behavior and customer issues. - Use LLM tools with practical **prompt engineering** for daily analytical tasks, summarization, and insights generation. - Ability to assess and profile LLM outputs for quality, relevance, and analytical correctness in business contexts. - Familiarity with **LLM APIs** and integrating model outputs into analytical or internal tooling workflows. - Work with Airflow and Python to automate data tasks and support workflows. - Optimize analytics pipelines and data marts to improve performance, resource efficiency, and reliability. - Identify and refactor inefficient DAGs, queries, and transformations to ensure scalable and cost-effective data processing. - Suggest and co-build improvements in agent tooling, ticket routing, and SLA tracking. - Translate business inefficiencies into trackable metrics and measurable outcomes. - Act as an analytical partner to **Operations**, **Support Management**, **QA**, and **Training**. - Work with **Data Engineering** to ensure clean, accurate, and well-modeled data pipelines. - Communicate findings clearly through presentations, visualizations, and concise documentation.