Tonal is a company redefining home gym experiences through AI-driven coaching and data analytics. As a Staff Data Engineer, you will be responsible for designing and scaling secure data systems that support the company's growth while ensuring compliance with regulatory standards.
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
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
- 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