Cadence ABA builds AI-native clinical data tools for ABA therapy, capturing session trials by voice, structuring the data, tracking mastery trends, and drafting billable session notes so clinicians can stay present with the child instead of the tablet. We're looking for an Analytics Engineer to help turn the clinical and operational data flowing through Cadence into trusted, well-modeled datasets that power our product and internal decision-making.
In this role, you'll work closely with data, engineering, and product teams to build the data infrastructure and models that make our clinical data reliable, queryable, and actionable - from trial-level ABA data to mastery trends, authorization tracking, and billing metrics.
What you'll do
- Design, build, and maintain data models and transformation pipelines (e.g., using dbt or similar tools) on top of our data warehouse
- Partner with product and clinical teams to define key metrics around session data, mastery tracking, and authorization usage
- Ensure data quality, lineage, and documentation so that every metric can be traced back to its source
- Build and maintain data pipelines that ingest structured clinical event data at scale
- Collaborate with engineers to expose clean, well-tested datasets via APIs or BI tools
- Monitor data pipeline health and troubleshoot data quality issues as they arise
What we're looking for
- 3+ years of experience in analytics engineering, data engineering, or a similar role
- Strong SQL skills and experience with a modern data stack (e.g., dbt, Snowflake, BigQuery, or Redshift)
- Experience building and maintaining ELT/ETL pipelines
- Comfort working with data modeling concepts (dimensional modeling, star schemas, etc.)
- Experience with version control and CI/CD for data pipelines
- Ability to communicate clearly with both technical and non-technical stakeholders
Nice to have
- Experience with healthcare or clinical data, especially in a HIPAA-compliant environment
- Familiarity with Python for data transformation or orchestration
- Experience working at an early-stage startup