Sr. Data Engineer

About the Company:

Octave is a modern behavioral health practice creating a new standard for care delivery that’s both high-quality and accessible. With in-person and virtual clinics in multiple states, the company offers evidence-based individual, couples, and family therapy, while pioneering relationships with payers to make care more affordable through insurance. By raising the bar on how care is delivered and how providers are supported, we are building a sustainable system that values equity, affordability, and effectiveness.

Job Summary:

We’re looking for a Sr. Data Engineer with strong data platform experience to help evolve our modern data stack and contribute to the foundation of our emerging AI and ML platform. This role sits at the intersection of data engineering, platform architecture and machine learning enablement and will bring high-quality, scalable, and ethical AI into real-world use. You will partner closely with data scientists, analysts, and product managers to ensure our platform supports reliable data pipelines, scalable analytics, in addition to defining new architecture, best practices, and patterns for fellow engineers to inherit. The ideal candidate is both a systems thinker and a hands-on builder who thrives in evolving environments and is passionate about creating reliable data infrastructure that enables peers and partner teams to move faster with data. You will have the opportunity to learn and contribute to a machine learning enablement and will bring high-quality, scalable, and ethical AI into real-world use.

Duties & Responsibilities:

• Design, build, and maintain scalable systems for ingestion, transformation, and storage of data, with a focus on testing and observability.
• Implement frameworks, tooling, and automation to safely increase development velocity.
• Develop foundational end-to-end AI/ML workflows from (1) source ingestion and preparation, (2) training and tuning, (3) experimentation and productionisation, and (4) downstream systems integration (EHR modules, micro-services, dashboards).
• Support iterative model development and production operations and observability (accuracy, drift, bias, fairness, reproducibility).
• Contribute to a culture of continuous improvement, knowledge-sharing and mentoring of peer engineers.
• Leverage AI tools as a core part of daily work (drafting, research, iteration) to improve efficiency, quality, and decision-making.

Required Skills:

• Proficiency in SQL and Python with strong familiarity towards modern data engineering frameworks, infrastructure, and tooling.
• Proficiency with data ops best practices, monitoring, pipeline automation, and CI/CD.
• Knowledge of modern compute and ML frameworks/libraries (i.e., Spark, TensorFlow, PyTorch, scikit-learn).
• Ability to build production APIs and services, inclusive of MCP servers that expose internal data/services to LLMs.
• A collaborative mindset, dependable execution, drive to reflect and improve, and humility to ask questions and learn.
• Comfort using AI tools in day-to-day workflows, with a willingness to continuously rethink and improve how work gets done.
• Curiosity and openness to experimenting with new tools and approaches; prior experience with AI tools is a plus.
• Nice to have:
  • Experience with foundational end-to-end AI/ML workflows from (1) source ingestion and preparation, (2) training and tuning, (3) experimentation and productionization, and (4) downstream systems integration (EHR modules, micro-services, dashboards).
  • Some knowledge of modern compute and ML frameworks/libraries (i.e., Spark, TensorFlow, PyTorch, scikit-learn).
  • Some experience with building production APIs and services, inclusive of MCP servers that expose internal data/services to LLMs.