Data Engineer
Avontix, New York, NY, United States
Job Type
Full-time
Description
Data Engineer
Chesterfield Office Hybrid or Remote
Why You'll Want to Join!
Join a leading Revenue Cycle Management (RCM) company dedicated to transforming healthcare data into actionable insights. We leverage cutting-edge technology to streamline financial and operational processes, improving efficiency and patient outcomes. We are looking for a
Data Engineer
to help optimize data pipelines and build a next-generation data infrastructure incorporating technologies such as
Microsoft Fabric, Azure Synapse, Databricks, and Snowflake .
Position Overview
Lead the modernization of our data infrastructure as a Data Engineer for nimble. You'll architect scalable cloud-native pipelines using Microsoft Fabric and Databricks to transform healthcare data-claims, EMR/EHR, HL7/FHIR-into actionable insights that drive revenue cycle optimization and clinical outcomes.
Why This Role Matters
Healthcare data engineering is mission-critical: clean, governed data flows directly impact financial accuracy, compliance, and the decisions that improve patient care. Your ETL/ELT pipelines enable our analytics and data science teams to unlock the full potential of healthcare data.
Key Responsibilities
• Design, build, and optimize ETL/ELT pipelines using Azure Synapse, Databricks, and Snowflake
• Develop robust data models and schemas for healthcare datasets, including claims, EMR/EHR, HL7, and FHIR standards
• Write and optimize SQL queries for performance across large healthcare datasets
• Implement data governance, quality frameworks, and HIPAA compliance controls
• Collaborate with analytics, data science, and business teams to define data requirements
• Monitor and troubleshoot data pipeline health and performance
• Develop Python or Scala code for complex transformations and data processing
• Support Power BI and analytics teams with data modeling and performance optimization
• Document data lineage, transformations, and technical architecture
Requirements
• 3+ years of professional data engineering or ETL/ELT development experience
• Expert-level SQL skills with proven optimization experience
• Proficiency in Python, Scala, or similar data processing languages
• Hands-on experience with cloud data platforms (Azure Synapse, Snowflake, Databricks, or equivalent)
• Understanding of healthcare data standards (HL7, FHIR, claims data structures)
• Strong grasp of data modeling, normalization, and schema design
• Experience with data versioning, CI/CD pipelines, and data quality frameworks
Preferred Qualifications
• Experience with Microsoft Fabric or Azure Data Factory
• Knowledge of HIPAA compliance and healthcare data security
• Background in healthcare, RCM, or claims processing
• Experience with dbt (data build tool) or equivalent transformation frameworks
• Exposure to dimensional modeling and data warehousing best practices
What Success Looks Like
• In 90 days: Deploy first cloud pipeline to production; complete HIPAA training; establish data quality baseline metrics
• In 6 months: Reduce data pipeline latency by 30%; expand healthcare data models to include new sources; build reusable transformation components
• Ongoing: Maintain 99.5%+ pipeline uptime; mentor junior engineers; drive architectural improvements for scale and performance
Full-time
Description
Data Engineer
Chesterfield Office Hybrid or Remote
Why You'll Want to Join!
Join a leading Revenue Cycle Management (RCM) company dedicated to transforming healthcare data into actionable insights. We leverage cutting-edge technology to streamline financial and operational processes, improving efficiency and patient outcomes. We are looking for a
Data Engineer
to help optimize data pipelines and build a next-generation data infrastructure incorporating technologies such as
Microsoft Fabric, Azure Synapse, Databricks, and Snowflake .
Position Overview
Lead the modernization of our data infrastructure as a Data Engineer for nimble. You'll architect scalable cloud-native pipelines using Microsoft Fabric and Databricks to transform healthcare data-claims, EMR/EHR, HL7/FHIR-into actionable insights that drive revenue cycle optimization and clinical outcomes.
Why This Role Matters
Healthcare data engineering is mission-critical: clean, governed data flows directly impact financial accuracy, compliance, and the decisions that improve patient care. Your ETL/ELT pipelines enable our analytics and data science teams to unlock the full potential of healthcare data.
Key Responsibilities
• Design, build, and optimize ETL/ELT pipelines using Azure Synapse, Databricks, and Snowflake
• Develop robust data models and schemas for healthcare datasets, including claims, EMR/EHR, HL7, and FHIR standards
• Write and optimize SQL queries for performance across large healthcare datasets
• Implement data governance, quality frameworks, and HIPAA compliance controls
• Collaborate with analytics, data science, and business teams to define data requirements
• Monitor and troubleshoot data pipeline health and performance
• Develop Python or Scala code for complex transformations and data processing
• Support Power BI and analytics teams with data modeling and performance optimization
• Document data lineage, transformations, and technical architecture
Requirements
• 3+ years of professional data engineering or ETL/ELT development experience
• Expert-level SQL skills with proven optimization experience
• Proficiency in Python, Scala, or similar data processing languages
• Hands-on experience with cloud data platforms (Azure Synapse, Snowflake, Databricks, or equivalent)
• Understanding of healthcare data standards (HL7, FHIR, claims data structures)
• Strong grasp of data modeling, normalization, and schema design
• Experience with data versioning, CI/CD pipelines, and data quality frameworks
Preferred Qualifications
• Experience with Microsoft Fabric or Azure Data Factory
• Knowledge of HIPAA compliance and healthcare data security
• Background in healthcare, RCM, or claims processing
• Experience with dbt (data build tool) or equivalent transformation frameworks
• Exposure to dimensional modeling and data warehousing best practices
What Success Looks Like
• In 90 days: Deploy first cloud pipeline to production; complete HIPAA training; establish data quality baseline metrics
• In 6 months: Reduce data pipeline latency by 30%; expand healthcare data models to include new sources; build reusable transformation components
• Ongoing: Maintain 99.5%+ pipeline uptime; mentor junior engineers; drive architectural improvements for scale and performance