• Design, develop, and optimize enterprise-scale ETL/ELT data pipelines • Build scalable data integration solutions for data warehouses and data lakes • Develop high-performance data processing workflows using Python and SQL • Design cloud-native data architectures using AWS, Azure, or GCP • Create and maintain reliable, secure, and scalable data infrastructure • Implement data governance, security, and quality standards • Optimize pipeline performance, scalability, and processing costs • Troubleshoot complex data engineering issues • Collaborate with business stakeholders to translate requirements into technical solutions • Mentor junior engineers and promote engineering best practices • Produce technical documentation for architecture, transformations, and development standards

• Join a pod-driven data team at Launchmetrics • Design and build data pipelines using PySpark and Databricks • Architect efficient Delta Lake table schemas • Work closely with product, QA, and other data engineers • Own code quality and participate in cross-pod initiatives

• Own the reliability, availability, and accuracy of our data infrastructure • Build, maintain, and improve our data pipeline using our modern data stack • Build centralized, durable, and reusable data models • Build and maintain a semantic layer with canonical dimension and metric definitions • Partner closely with Analysts, PMs, Engineers, Marketing, Legal, Fraud, and other stakeholders • Build Reverse ETL pipelines in Python • Proactively bring in new data sources • Champion data governance and help elevate the organization's data maturity

• Design and build full-stack features spanning React frontends, Python/FastAPI backends, and supporting data models and transformations • Develop and maintain data models and transformation pipelines (dbt preferred) that feed application and analytics layers; ensure data flowing into applications is well-modeled, tested, and reliable • Design and implement REST APIs serving clinical data, quality metrics, care gaps, and decision support content to internal applications and external partners • Implement API contracts, versioning strategies, authentication/authorization patterns (OAuth/OIDC), and rate limiting for compliant clinical data access • Build responsive, accessible React user interfaces with modern component patterns; collaborate with product and clinical teams to translate requirements into intuitive UIs • Design and implement comprehensive testing strategies — unit tests, integration tests, end-to-end tests, and data validation tests — to ensure reliability across the stack • Conduct and support QA activities including test planning, test case design, manual testing, and establishing testing standards; work closely with QA engineers and clinical testers to validate functionality and user experience • Write clean, tested, maintainable code across the stack; participate actively in code review and help raise code quality standards • Use AI-assisted development tools (Claude Code, GitHub Copilot, Cursor, or similar) deliberately and effectively — leveraging them for scaffolding, refactoring, test generation, and documentation while maintaining code quality and understanding • Define and measure success metrics for features — including usage, adoption, clinical workflow impact, and data quality — to drive iterative improvements and prioritization • Partner with product and data teams to establish KPIs and dashboards that measure feature impact on clinician workflows, care coordination, and operational efficiency • Troubleshoot and resolve issues across the full stack — from UI bugs to API failures to data pipeline problems; trace issues end-to-end and implement durable fixes • Collaborate with data engineering to ensure API data contracts are well-defined and upstream data models support application needs • Participate in architecture and design discussions including API design, authentication patterns, data contract definition, and system reliability • Raise data quality or data modeling concerns early in the development process rather than letting them surface downstream • Contribute to technical documentation including API specifications, data dictionaries, runbooks, and architectural decisions • Support production systems through on-call rotations, incident response, and post-incident improvements • Mentor junior engineers and contribute to team process improvement and knowledge sharing