Data Engineer – Healthcare Analytics Platform

Guidehouse is a consulting firm that specializes in data engineering and analytics solutions for various sectors, including healthcare. They are seeking a Data Engineer to support the design and development of an enterprise Contract Performance Analytics platform, focusing on data architecture, pipeline development, and integration of healthcare data.

Responsibilities:

• Design, develop, and maintain robust ETL/ELT pipelines to ingest, transform, and load healthcare data from diverse structured and unstructured sources
• Develop pipelines to process data from CMS and payer files (CCLF, paid claims, PUG) as well as Epic (Caboodle, Clarity) data models and extracts
• Build and optimize data models to support analytics, reporting, and operational use cases, including BI and downstream analytics consumption
• Transform raw data into standardized, analytics-ready canonical data models and curated data marts
• Build lakehouse/medallion architecture, data ingestion patterns, and orchestration frameworks
• Implement and maintain CI/CD pipelines for data engineering workflows, including pipelines and scheduled jobs, using version control and automation tools
• Collaborate with database administrators, analysts, and application teams to integrate data sources, design schemas, and support downstream data consumers
• Ensure data quality, integrity, and accuracy through validation, monitoring, logging, and alerting
• Support data migration, integration, and modernization initiatives, including legacy system upgrades, optimization of large-scale ETL pipelines, query performance, and cloud adoption efforts
• Troubleshoot and resolve issues in development and production environments to maintain stable and reliable data pipelines
• Document data flows, pipelines, test cases, and technical solutions to support knowledge sharing and compliance requirements
• Stay current with emerging tools, technologies, and best practices in data engineering and cloud platforms

Requirements:

• US Citizenship or a Green Card is required
• Bachelor's degree in Computer Science, Data Analytics, Software Engineering, Information Systems, or related fields
• A minimum of FIVE (5) years of experience in data engineering, ETL/ELT development, or data platform engineering in a healthcare setting
• Experience working with healthcare data, including claims, clinical, payer, or population health datasets
• Experience working with healthcare data interoperability standards (e.g., FHIR, HL7)
• Proficiency in Python and SQL for data engineering and transformation workloads
• Hands-on experience designing and building ETL/ELT pipelines and data ingestion frameworks
• Experience working with modern cloud data platforms or ETL/ELT tools (e.g., Databricks, Azure Data Factory, AWS Glue)
• Experience working with lakehouse or medallion-style architectures for analytics platforms
• Strong knowledge of relational database design, data warehouses, and/or data lakes (e.g., star/snowflake schemas)
• Experience working with relational and/or distributed data systems, including data modeling
• Experience working in a cloud environment (AWS or Azure) supporting data solutions
• Experience with CI/CD practices and version control tools (e.g., Git)
• Experience using monitoring and logging tools to support data pipeline reliability
• Experience working with PHI and healthcare data privacy/security requirements
• Ability to work effectively in an Agile development environment
• Strong analytical and troubleshooting skills, and the ability to communicate technical concepts clearly to clients, engineers, and business stakeholders
• Ability to work independently in a fast-paced, client-facing environment
• Previous experience working with Epic and/or Athena in a healthcare setting for data engineering
• Previous experience with Exasol or similar analytics platform
• AWS, Azure, Databricks, Snowflake or other data engineering–related certifications
• Experience with data visualization or analytics tools (e.g., Tableau, Power BI)
• Exposure to microservices-based architectures or AI/ML-enabled data pipelines
• Prior consulting experience