• Drive Prompt’s mission to improve healthcare through high-quality, actionable data that informs client and internal decision-making • Own the design, development, and iteration of complex data systems — from raw data ingestion through transformation, modeling, and downstream analysis • Perform exploratory analysis and hypothesis-driven investigations to surface insights and guide both client-facing and internal decision-making • Leverage modern AI and LLM-powered tools (e.g., code assistants, agents, automation frameworks) to accelerate data transformation, analysis, and iteration — while maintaining high standards for reliability, security, and maintainability • Develop and evolve well-modeled datasets, complex transformations, and metrics • Design and implement data quality checks, monitoring, and observability to ensure correctness, freshness, and trust in data products • Partner with stakeholders across the company to understand client and internal workflows, define success criteria, and deliver data products to drive improvement

• Own the systems that turn raw data into trusted insights across the company • Partner closely with Product, Finance, Sales, and Customer Success to ensure our data platform scales with the business and directly supports decision-making • Design, build, and maintain trusted, scalable data models that power reporting, analytics, and product insights • Own the evolution of our data platform, unifying analytics tooling and fully replacing legacy admin and ad-hoc reporting • Develop and maintain robust ELT pipelines, transformations, and data stores that serve as the source of truth for billing, revenue, usage, and customer success workflows • Apply strong data modeling and warehousing principles to create analytics-optimized tables that are well-documented and easy to use • Partner with stakeholders to understand business questions, translate them into data models, and educate teams on how to operationalize analytics effectively • Proactively identify opportunities to improve reporting or data reliability and build proof-of-concepts to validate solutions with the business • Write high-quality, secure, maintainable, and testable production code, following best practices and thoughtful tradeoffs • Contribute to a culture of craftsmanship through code reviews, documentation, and continuous improvement • Balance speed and quality, delivering quickly while building systems that scale with the company • Adapt to a wide range of challenges, learning new tools and skills as needed in a fast-moving environment

• Building and maintaining data pipelines and workflows using Cloud Composer • Designing and implementing data models, database schemas, and ETL processes using core GCP data services like BigQuery, Dataflow, Pub/Sub, Cloud Storage, Cloud Functions • Collaborating with cross-functional teams to understand business requirements and develop solutions • Ensuring data quality and reliability by monitoring and debugging data pipelines • Maintaining and improving our existing data infrastructure and processes • Staying up-to-date with industry trends and best practices in data engineering.

• architect and develop scalable data pipelines and models using Snowflake and dbt • design and operate orchestration using Airflow, Dagster, Prefect, or similar • define modeling standards, storage patterns, and performance strategies • collaborate on backend APIs, services, integration layers, and limited frontend interfaces supporting data-driven features • contribute to CI/CD, environment management, and automated pipeline delivery • implement quality checks, lineage, SLAs, and observability • mentor engineers and drive architectural best practices • participate in system design and multi-team architectural planning