• Lead the vision, strategy, and roadmap for Natera’s health system data sharing initiatives, including platforms that integrate Natera’s genomic data with health system clinical data to enable advanced analytics, clinical insights, and operational intelligence. • Identify and prioritize high-value opportunities for secure data exchange, interoperability, and analytics across health systems, including use cases that support clinical decision-making, research, and population health. • Collaborate with engineering, data science, and design teams to define product requirements (PRDs, user stories, user flows) and deliver scalable, high-quality data platform, analytics, and AI-driven insights solutions. • Develop products and user experiences that enable clinicians and health systems to derive insights and actions from combined genomic and clinical datasets, including dashboards, workflow integrations, and AI-assisted recommendations. • Evaluate and integrate third-party technologies (e.g., data interoperability frameworks, analytics platforms, and AI/ML tools) to accelerate development and improve performance. • Develop and track success metrics (e.g., adoption, data utilization, clinical impact, performance, and partner satisfaction) to measure impact and identify continuous improvement opportunities. • Partner with health system stakeholders, clinical informatics teams, and business leaders to ensure seamless integration into clinical workflows and EMR ecosystems.

• Design, configure, and maintain AI agent workflows using Cursor and Claude Code, including structured markdown-based instruction files, slash commands, and agent workflows that implement your architecture automatically. • Research and evaluate new approaches to agent orchestration, identifying where the current system can be made faster, more reliable, or more scalable. • Continuously question existing processes and propose improvements. This is expected, not exceptional. • Guide and review AI-generated code with a critical eye; you are the architect, the agents are the executors. • Build and maintain a RAW → Base → Data Marts pipeline using dbt Core, implementing business logic at the transformation layer. • Own data pipelines end to end, from API ingestion through incremental strategy, transformation, and QA, with full accountability for architectural decisions. • Write and maintain tests and documentation at every layer of the pipeline. • Connect to and pull data from marketing and e-commerce APIs including Meta, TikTok, Shopify, and Klaviyo. • Build comprehensive test suites using Great Expectations or equivalent tooling. • Implement business logic validation checks and automated quality monitoring across the pipeline. • Manually inspect data at the table level, not only through automated scripts, before presenting any conclusions. • Verify your own work as a default behavior, not a step that needs to be requested. • Implement complex business logic and data validation in Python. • Build ETL scripts, automation workflows, and API integration scripts. • Write code that is built to scale, not just to solve the immediate problem.

• Join our engineering team and be a valued member working closely in a collaborative, autonomous, cross-functional team • Build and maintain scalable data processing platforms and services on GCP • Design and develop event-driven architectures using GCP-native services • Develop and maintain data pipelines and backend services using Python • Contribute to infrastructure automation and platform provisioning using Terraform • Support containerised workloads running on GKE • Implement monitoring, observability, and operational best practices • Collaborate closely with product and engineering teams to deliver reliable platform capabilities • Participate in platform support and operational ownership activities • Improve testing, deployment, and software delivery practices • Contribute to architectural discussions and long-term platform evolution

• Design and implement ETL processes in a cloud environment • Design and implement monitoring solutions for ETL processes • Document datasets for data warehouse consumers • Serve as a technical resource for consumers of the data warehouse, in particular the Data Science Team and other internal business units