Healthcare IT & AI/ML Integration Data Engineer

The Digital Modernization Sector is seeking an experienced Healthcare IT & AI/ML Data Engineer to join our team in modernizing data collection, aggregation, and analysis for the Centers for Medicare and Medicaid Services (CMS). As an IT integrator contractor supporting application development teams and business owners, we aim to enhance data transformation functionality while leveraging AI/ML-driven solutions for intelligent data modernization.

This role requires a deep understanding of data engineering, AI/ML integration, cloud-based data solutions, and compliance with federal healthcare regulations. The ideal candidate will assess current implementations, identify areas for optimization, and propose strategic improvements that align with modern data engineering best practices and CMS objectives.

Key Responsibilities:

• Assessment & Strategy:
  • Evaluate existing data pipelines, architectures, and transformation processes.
  • Provide recommendations for optimizing and modernizing data systems to enhance efficiency, scalability, and cost-effectiveness.
  • Define a data modernization strategy that incorporates AI/ML-driven automation and analytics.
• Data Engineering & Transformation:
  • Design and implement scalable, cloud-native ETL/ELT pipelines to support real-time and batch data processing.
  • Build and maintain data warehouses and data lakes for high-performance analytics and reporting.
  • Improve data quality, lineage, and governance with automated validation and monitoring.
  • Implement data versioning and reproducibility for better traceability.
• AI/ML Integration for Data Modernization:
  • Develop and integrate AI/ML solutions to enhance data ingestion, transformation, and analytics capabilities.
  • Work with ML frameworks (TensorFlow, PyTorch, MLflow) to enable automated decision-making and anomaly detection.
  • Collaborate with Data Scientists to operationalize machine learning models and optimize predictive analytics.
  • Utilize NLP and deep learning to improve healthcare data processing and extraction.
• Cloud & Infrastructure Modernization:
  • Architect cloud-based data solutions leveraging AWS, Azure, or Google Cloud (e.g., AWS Glue, Redshift, S3, Azure Synapse).
  • Implement serverless and event-driven architectures for dynamic data processing.
  • Ensure high availability and security in alignment with CMS and federal compliance standards (FISMA, HIPAA).
• Data Governance & Compliance:
  • Ensure adherence to CMS data policies, federal security regulations, and privacy frameworks (HIPAA, FHIR, 21st Century Cures Act).
  • Design data models with strong governance principles to improve auditability and regulatory reporting.
  • Establish best practices for data cataloging and metadata management.
• Collaboration & Agile Practices:
  • Work closely with business analysts, software engineers, data scientists, and DevSecOps teams to develop end-to-end solutions.
  • Participate in Agile/Scrum development cycles, contributing to sprints and backlog grooming.
  • Document technical processes, workflows, and data architecture for cross team knowledge sharing.

Required Skills & Qualifications:

• Technical Expertise:
  • Proficiency in SQL, Python, and Scala for data transformation and automation.
  • Experience with big data processing frameworks (Apache Spark, Databricks, Hadoop).
  • Hands-on experience with ETL/ELT orchestration tools (Apache Airflow, dbt, Informatica).
  • Strong knowledge of cloud-based data platforms (AWS Glue, Redshift, Azure Data Factory, Google BigQuery).
  • Familiarity with containerization and orchestration (Docker, Kubernetes).
  • Experience with real-time data streaming (Kafka, Kinesis, Pub/Sub) for handling large-scale data ingestion.
  • Solid understanding of DataOps methodologies, CI/CD pipelines, and Infrastructure-as-Code (Terraform, CloudFormation).
• AI/ML & Advanced Analytics:
  • Strong understanding of ML engineering for integrating AI models into data pipelines.
  • Experience with ML lifecycle management using MLflow, TensorFlow Extended (TFX), or Kubeflow.
  • Proficiency in feature engineering and model deployment in cloud environments.
  • Knowledge of NLP and predictive modeling techniques for healthcare applications.
• Core Consulting Skills
  • Excellent verbal and written communication