Staff Data Engineer

Tonal•San Francisco, CA

36d

About The Position

Tonal is the smartest home gym and personal trainer. With cutting-edge hardware, AI-driven coaching, and the world's largest dataset of real-world strength training, we're redefining how people train. Our platform combines data from millions of workouts, sensors, and integrations to deliver personalized training, enhance coaching, and measure progress in entirely new ways. We're passionate about transforming lives through strength, performance, and health. At Tonal, innovation, science, and data come together to create a training experience that's personal, motivating, and measurable. Overview As a Staff Data Engineer, you will shape the backbone of Tonal's data platform. You'll design and scale systems that bring together massive volumes of workout, sensor, and health-related data while ensuring security, reliability, and trust. This role requires a deep understanding of compliance and security standards and the ability to build infrastructure that protects sensitive information while fueling product innovation, AI, and analytics.

Requirements

8+ years of experience in data engineering, or 6+ years with a Master's degree (or equivalent).
Strong skills in SQL, Python, and distributed data processing (Spark, Databricks, or similar).
Experience building pipelines with DBT, Airflow, Fivetran, or related tools.
Background in data modeling and warehousing with systems like Snowflake, Databricks, or Redshift.
Hands-on experience working with regulated environments and sensitive data.
Familiarity with frameworks such as HIPAA, SOC 2, and NIST for security and compliance.
Skilled in access control design, audit logging, encryption, and governance.
Excellent communicator who can explain complex tradeoffs to both technical and non-technical audiences.
Known for technical leadership and mentoring, raising the bar for engineering quality.

Nice To Haves

Experience with fitness, healthcare, IoT, or sensor data.
Knowledge of privacy-preserving techniques (k-anonymity, l-diversity, differential privacy).
Exposure to production ML/AI pipelines involving sensitive data.
Background in connected fitness, digital health, or regulated healthcare products.

Responsibilities

Architect secure and scalable data systems that support Tonal's growth and meet regulatory standards.
Build and optimize data models and pipelines across diverse sources: sensors, workouts, health integrations, CRM, payments, and content.
Establish controls for access, encryption, anonymization, monitoring, and auditability.
Define and enforce best practices for managing sensitive data, including PHI and PII.
Collaborate with teams across Product, Engineering, Sports Science, and Healthcare to translate needs into compliant solutions.
Conduct risk assessments and implement safeguards guided by NIST frameworks.
Support SOC 2 audits by documenting and demonstrating effective security controls.
Mentor engineers and scientists, setting high standards for secure data engineering.
Continuously evolve the platform, introducing new tools and frameworks to balance innovation with strong regulatory posture.

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