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As a Senior Customer Experience (CX) Data Analyst at Tabby, you will drive data-informed improvements to customer support operations through analytics, reporting, and process optimization. You'll partner closely with CX, product, operations, and support management teams to measure performance, identify bottlenecks, and enable better decision-making across support workflows.
Key responsibilities include analyzing large-scale datasets on agent performance, support interactions, and quality assurance metrics. You'll write optimized SQL queries in BigQuery and ClickHouse, build dashboards in Tableau or Metabase, and detect behavioral and performance trends across support teams. Working with the QA team, you'll identify root causes in agent behavior and customer issues, translating business inefficiencies into trackable metrics and measurable outcomes.
You'll leverage LLM tools and prompt engineering for daily analytical tasks, summarization, and insights generation. This includes assessing LLM output quality, familiarity with LLM APIs, and integrating model outputs into analytical workflows. You'll also work with Airflow and Python to automate data tasks, optimize analytics pipelines and data marts for performance and cost-efficiency, and refactor inefficient DAGs and queries to ensure scalability.
Cross-functional collaboration is central to the role. You'll act as an analytical partner to operations, support management, QA, and training teams, work with data engineering to ensure clean and well-modeled pipelines, and communicate findings clearly through presentations, visualizations, and documentation.
Required qualifications: 3+ years as a data analyst in fast-paced, data-heavy environments; advanced SQL skills (BigQuery/ClickHouse preferred); Python proficiency for data analysis and automation; experience with data visualization tools (Tableau, Metabase, Looker); understanding of Airflow and modern data pipelines; experience with LLM agents and prompt engineering; ability to translate business questions into testable hypotheses; strong presentation and storytelling skills for technical and non-technical audiences; upper-intermediate or higher English proficiency.
