Design, maintain, and optimize cloud-based data infrastructure for advanced analytics and machine learning. Collaborate with data scientists and product owners to deliver reliable data assets. Ensure system uptime, scalability, and performance.
Key Highlights
Technical Skills Required
Benefits & Perks
Job Description
Title: Data Engineer - Clinical Data Science Team
Openings: 1
Duration: 12mo contract to hire
Hourly Pay: $55-65/hr approx.
Schedule: REMOTE anywhere in the US, Mon-Fri day shift (core hours, EST/CST)
Start Date: ASAP
Interview Process: 2-3 rounds
Required Qualifications
- 7+ years of experience in data engineering or data architecture, preferably in healthcare, life sciences, or payer environments.
- Expertise in Google Cloud Platform (GCP), including BigQuery, Dataflow, Cloud Composer (Airflow), and related services.
- Proficiency in Python, SQL, and modern data pipeline frameworks (e.g., Apache Beam, Airflow, or dbt).
- Experience supporting machine learning workflows and productionizing data science models.
- Proven ability to collaborate with cross-functional teams and translate technical solutions into business impact.
- Strong understanding of data governance, security, and PHI/PII compliance in regulated healthcare environments.
Preferred Qualifications
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or a related technical field.
- Experience with MLOps and model lifecycle management frameworks (e.g., Vertex AI, Kubeflow, MLflow).
- Familiarity with Medicare Stars, HEDIS, or patient safety data domains and associated CMS data sources.
- Background in designing real-time or near-real-time data solutions supporting quality and clinical programs.
Position Summary
The Data Engineer for Clinical Data Science Team is a key technical leader responsible for designing, maintaining, and optimizing the cloud-based data infrastructure that powers advanced analytics and machine learning for Aetna’s Medicare Stars programs. This role provides foundational GCP engineering and pipeline support for the data science model-building team, ensuring the stability, scalability, and performance of production analytics environments.
The ideal candidate combines deep technical expertise with a consultative, solution-oriented mindset—partnering closely with data scientists, analysts, and product owners to deliver reliable data assets and enable high-impact modeling for patient safety, medication adherence, and quality improvement initiatives.
Key Responsibilities
Data Engineering & Platform Management
- Architect, implement, and maintain data pipelines, ETL workflows, and analytical environments on Google Cloud Platform (GCP) to support predictive modeling and clinical data science initiatives.
- Develop and manage structured, version-controlled data assets optimized for machine learning, segmentation, and decile model development.
- Ensure end-to-end data integrity, lineage, and reproducibility across ingestion, transformation, and model deployment layers.
- Monitor and maintain production pipelines and batch processes to guarantee system uptime, scalability, and performance.
- Build and maintain automation to support continuous data availability and model refresh cycles for Medicare Stars, Patient Safety, and PDP initiatives.
Collaboration & Consultative Partnership
- Act as the primary engineering partner to data scientists—translating analytical requirements into efficient, maintainable cloud data structures.
- Serve as a consultative resource on data architecture, model operationalization, and best practices for model lifecycle management.
- Collaborate with enterprise data engineering, IT security, and compliance teams to ensure governance, quality, and regulatory adherence.
- Partner with analytics leadership to identify emerging technology and data opportunities that enhance analytical agility and value creation.
Technical Operations & Maintenance
- Oversee monitoring, alerting, and root cause diagnostics for pipeline failures and latency issues.
- Regularly audit system performance, cloud costs, and data dependencies to proactively address technical risks.
- Maintain model-serving infrastructure and support deployment workflows (batch or API) to production environments.
- Support versioning, rollback, and documentation standards for analytical assets and ML models.
Core Competencies
- Technical Leadership: Deep cloud engineering skill set and system ownership mindset.
- Consultative Partnership: Ability to guide data scientists and leaders toward scalable, sustainable solutions.
- Operational Excellence: Proactive monitoring, reliability engineering, and automation.
- Innovation: Forward-thinking in applying cloud-native tools, AI integration, and cost-efficient architectures.