Freelance Data Engineer / ML Platform Engineer

About the Project

We are building an AI-powered healthcare claims intelligence platform for a leading healthcare payer organization. The objective is to identify suspicious coding patterns, prevent AI-driven over-coding, and enable real-time claim risk detection before adjudication.

The platform will leverage cloud-native data engineering, machine learning operations, and advanced analytics to help automate decision-making and reduce claim leakage.

Key Responsibilities

• Design and develop scalable data pipelines on AWS.
• Build and optimize data workflows using Databricks.
• Develop and manage enterprise data models in Snowflake.
• Implement and maintain ML lifecycle management using MLflow.
• Support real-time and batch data processing workloads.
• Collaborate with data scientists and business stakeholders to operationalize machine learning models.
• Ensure data quality, governance, security, and performance optimization.
• Build automated monitoring and deployment pipelines for data and ML workloads.

Required Skills

Strong hands-on experience with AWS Cloud Services

Experience working with Databricks (mandatory)

Good exposure to Snowflake Data Warehouse

Experience with MLflow for model tracking and deployment

Python and SQL expertise

Data Pipeline Development & ETL/ELT frameworks

Experience with Spark / PySpark

Understanding of MLOps concepts and CI/CD practices

Preferred Skills

• Healthcare, Insurance, Claims, or RCM domain experience
• Real-time analytics and streaming data exposure
• Knowledge of AI/ML model deployment and monitoring
• Experience with Delta Lake and Lakehouse architecture

Experience Required

5+ Years of Overall Experience

3+ Years in AWS Data Engineering

Hands-on Databricks and Snowflake implementation experience

What We're Looking For

We are seeking a highly skilled freelancer who can bridge Data Engineering and ML Platform capabilities. Candidates with strong AWS, Databricks, Snowflake, and MLflow expertise will be highly preferred.

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