Sr. Data Engineer

Octave
Denver, US
On-siteCareer-pivot friendly

Why this role

Pace
Fast Paced
Collaboration
High
Autonomy
Medium
Decision Impact
Team
Role Level
Individual Contributor
Career Pivot Friendly
Welcomes transferable skills

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • built scalable data pipelines
  • developed AI/ML workflows
Typical background
data engineeringplatform engineering

Transferable backgrounds

  • Coming from data scientist
  • Coming from AI engineer

Skills & requirements

Required

Data EngineeringAi/mlPythonSQLCloud PlatformsData Ops

Preferred

HealthcareBehavioral Health

Stack & domain

SQLPythonData EngineeringPlatform ArchitectureMachine LearningAIMLData OpsMonitoringPipeline AutomationCI/CDSparkTensorFlowPyTorchscikit-learnApisServicesLlmsData VisualizationCollaborationMentoringCuriosityOpennessTeamworkHealthcareBehavioral HealthEhr

About the role

Original posting from Octave

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, and production ready machine learning systems 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.

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 productionization, 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.

Education & Experience:

  • Bachelor's degree (or equivalent) in Computer Science, Data Science, Statistics, Engineering or a related field.
  • 5+ years of experience in data engineering, platform engineering, or ML engineering.
  • Experience working with major cloud data platforms and tools:
  • Preferred experience:
  • Healthcare, behavioral health, EHR systems, and/or regulated industries.
  • Specific expertise with: AWS/GCP, dbt, AirflowAirbyte, Redshift/BigQuery.

Octave's Company Values:

The below values drive our day-to-day operations.

  • We're human beings first. We operate with empathy and kindness – with our clients, with our collaborators, and with ourselves.
  • People deserve better than status quo. We're willing to tackle the intractable problems, no matter how big, because someone should. We ask big questions, we craft big solutions, and we challenge ourselves and others to make it happen.
  • No bystanders. No stars. No tourists. Each person has been selected to be here, and with that comes a responsibility to bring your expertise, share your ideas, and help make this company better.
  • Partnership paves the path ahead. We don't operate in a silo, internally or externally. To transform the system, we believe in working with others to create something bigger, better, and stronger.
  • Quality is crucial at scale. Quality is core to our business, and we refuse to sacrifice it as we grow.
  • Progress is a process. In the pursuit of progress, we iterate, reflect, learn, adjust – and always leave things better than we found them.
  • There are people behind every data point. We recognize that numbers tell only one part of the story, and we also do the work to understand impacts at the individual level.

Physical Requirements:

  • Prolonged periods sitting at a desk and working on a computer.
  • Must be able to frequently communicate

Source: Octave careers

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