data engineer for health coaching

Simple is an AI-powered health coaching app that helps adults lose weight and build healthier habits through personalized, sustainable support. The health tech product has surpassed 17 million downloads and 300,000 five-star reviews.

• Design and implement scalable, reliable data warehouse architecture, including data models, storage strategies, and processing layers;
• Develop and maintain a role-based access control model for secure and appropriate organizational data access;
• Design, implement, and maintain inbound and outbound data pipelines with reliability, observability, and data integrity;
• Optimize SQL queries, data models, and compute and storage resource usage for cost-efficiency and high performance;
• Implement and enforce data protection and security best practices for sensitive and regulated data;
• Work with analytics and business teams to deliver efficient, scalable, and reliable data solutions;
• Improve and automate data governance, including data quality controls, metadata management, lineage, and compliance processes;
• Monitor and troubleshoot data pipelines and platform issues, ensuring stability and timely incident resolution;
• Continuously improve data platform architecture, tooling, and development practices.

• Advanced SQL expertise, including query optimization, execution analysis, and physical-level understanding;
• Strong commercial experience working with real-world data and designing systems that tolerate and prevent data quality issues;
• Solid understanding of data flows and data lifecycle, including detection and prevention of downstream problems caused by unexpected or malformed data;
• Proven experience designing and building data warehouses and understanding common architectural patterns;
• Experience implementing ETL/ELT pipelines in production environments;
• Good understanding of cloud platforms and distributed data systems;
• Strong Python skills for data processing, orchestration, and automation;
• Experience working with cloud data warehouses;
• Familiarity with BI and analytical tools;
• Ability to work with large-scale datasets and production-grade data platforms;
• Nice to have: Hands-on experience with Airflow, experience using dbt for transformations and data modeling, practical experience with AWS data stack, experience working with sensitive or regulated data, experience building data quality, observability, or data governance frameworks.

• Competitive salary package based on expertise, skillset, and product impact, plus stock options;
• In-office, remote, and hybrid work opportunities;
• Equipment provided according to individual needs;
• Premium SIMPLE subscription;
• 21 Days of annual leave plus bank holidays observed where the employee lives;
• Flexible hours focused on results.