About the role

Role Overview

Sr. Data Engineer at Octave, partnering with data scientists, analysts, and product managers to evolve a modern data stack and help build the foundation for an emerging AI/ML platform. The role focuses on scalable, reliable, and ethical AI in production.

Responsibilities

Design, build, and maintain scalable data systems for ingestion, transformation, and storage, with emphasis on testing and observability.
Implement frameworks, tooling, and automation to safely increase engineering velocity.
Build end-to-end AI/ML workflows, including:
- Source ingestion & data preparation
- Training & tuning
- Experimentation & productionization
- Integration with downstream systems (e.g., EHR modules, microservices, dashboards)
Support iterative model development and production operations/observability, including accuracy, drift, bias, fairness, and reproducibility.
Contribute to continuous improvement, knowledge-sharing, and mentoring.
Use AI tools as part of daily work to improve efficiency, quality, and decision-making.
Build production-ready APIs and services, including MCP servers to expose internal data/services to LLMs.

Required Skills

Strong SQL and Python; modern data engineering frameworks, infrastructure, and tooling.
DataOps best practices: monitoring, pipeline automation, and CI/CD.
Knowledge of modern compute/ML libraries: Spark, TensorFlow, PyTorch, scikit-learn.
Collaborative, dependable execution; drive to reflect and improve; humility to learn.
Comfort using AI tools day-to-day; willingness to rethink and improve workflows.

Nice-to-Haves / Preferred

Experience with healthcare, behavioral health, EHR systems, or other regulated industries.
Specific experience with: AWS/GCP, dbt, Airflow, Airbyte, Redshift/BigQuery.

Education & Experience

Bachelor’s degree (or equivalent) in Computer Science, Data Science, Statistics, Engineering, or related field.
5+ years of experience in data engineering, platform engineering, or ML engineering.

About Octave

Octave is a modern behavioral health practice delivering high-quality, accessible therapy through in-person and virtual clinics across multiple states. It also partners with payers to make care more affordable through insurance, aiming to improve equity, affordability, and effectiveness in care delivery. The company is building an AI/ML-enabled platform alongside its clinical and payer-facing work.

Tags