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.

Requirements

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, Airflow, Airbyte, Redshift/BigQuery.
Proficiency in SQL and Python
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.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data engineeringplatform engineeringML engineeringSQLPythondata ops best practicespipeline automationCI/CDSparkTensorFlow

Soft Skills

collaborative mindsetdependable executiondrive to reflect and improvehumility to ask questions and learnknowledge-sharingmentoring