Data Engineer 2

• Design, develop, and maintain scalable

  ETL/ELT pipelines

  to support AI/ML workflows in the RCM space.
• Work with

  structured and semi-structured data

  from diverse healthcare and financial systems.
• Build and optimize

  data ingestion and transformation logic

  using

  PySpark

  ,

  Python

  , and

  SQL

  .
• Implement and manage

  DataOps pipelines

  , including

  data quality checks

  ,

  logging

  ,

  monitoring

  , and

  automated testing

  .
• Schedule and orchestrate pipelines using tools like

  Airflow

  ,

  Databricks Workflows

  , or

  Azure Data Factory

  .
• Optimize SQL queries and Spark jobs to ensure performance and cost-efficiency.
• Collaborate with data scientists to ensure timely delivery of clean, well-structured data for model development and deployment.
• Ensure adherence to data governance, security, and compliance policies in healthcare data environments.

Must-Have Skills:

• Strong proficiency in

  Python

  and

  PySpark

  for data processing
• Advanced SQL skills, including

  query optimization

  and complex joins
• Hands-on experience with

  data pipeline tools

  (e.g., Airflow, Databricks, ADF, Glue)
• Experience building

  data pipelines for AI/ML use cases

• Good understanding of

  data modeling

  ,

  data validation

  , and

  error handling

• Familiarity with

  healthcare or RCM data

  structures (claims, encounters, EDI 837, etc.) is a plus

Nice-to-Have Skills:

• Knowledge of

  cloud data platforms

  (e.g., AWS Redshift, Azure Synapse, Snowflake)
• Experience with

  CI/CD for data pipelines

• Exposure to

  data versioning tools

  (e.g., DVC) or

  data cataloging systems

• Familiarity with

  ML lifecycle and MLOps

  requirements

Educational Qualifications:

• Bachelor s degree in Computer Science, Information Systems, Data Engineering, or related field
• Certifications in cloud platforms (AWS, Azure) or data engineering tools are a plus