• You sit between the business (Product Owner, stakeholders) and the engineering team. • About 30% of your time is practical data work: validating that CDP capabilities behave as specified, exploring data before specs are written, testing hypotheses for new capabilities, building shared gold layer models that consumer teams use, and helping those teams understand what data is available. • It is not business reporting or analytics built for individual brands. • The other 70% is Systems Analysis: turning what you find into specs engineers can build from, owning data quality from symptom to root cause, raising what isn’t right before it ships, and shaping the semantic layer so other business lines can find and trust the data. • Four things define the systems analysis side: Turn business intent into specs that engineers can build from. Talk to stakeholders, figure out what they actually need, and write it down. Each spec covers source schema, target model, transformation logic, business glossary, schedule, data quality checks, consumer, and use case. • Stay reachable through development. Engineers will have questions. Find and fix data quality problems at the root, alongside the engineering function. • Trace lineage, identify the cause, work with the data source owner on the fix, check downstream impact, and close the loop. • Confirm what’s delivered does what we said it would. Validate against the spec you wrote. When the data isn’t right, you raise it before it ships. • Make data usable beyond the team that asked for it. Contribute to the semantic layer, data descriptions, and shared definitions so other business lines can find what they need, understand what it means, and trust it without going through you each time. • You work on the Data Platform layer: CDP build, identity resolution, data quality, semantic definitions, and governance. Business analytics on top of the platform, such as dashboards and performance reports for specific brands, is produced by analyst teams aligned to those brands. Your job is to make sure those teams can do theirs: trustworthy data, clear definitions, fast issue resolution.

• quantitative analysis and predictive modeling of healthcare data to provide management with statistical findings and conclusions • develop, collect and analyze metrics and data to formulate fact-based decisions • proactively implement process improvements through analysis of claim and operational data

• 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.