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Data Pipeline Development: Design, build, and maintain robust ETL/ELT pipelines using dbt, Python, and SQL to ingest and transform healthcare data from diverse sources. Cloud Data Engineering: Work within AWS and Snowflake environments to manage data storage, processing, and access for analytics and operational use cases. Collaboration: Partner with data scientists, analysts, and application developers to deliver clean, well-modelled data that supports AI/ML, reporting, and business intelligence. Healthcare Data Integration (Preferred): Develop solutions to integrate healthcare revenue cycle data (e.g., 835/837 files, claims, and denial data) into centralized data platforms. Data Quality and Governance: Implement data validation, lineage tracking, and governance practices to ensure accuracy, compliance, and traceability. Operational Enablement: Deliver curated datasets from Snowflake to support downstream systems such as Artiva, applications, and reporting tools. Continuous Improvement: Contribute to the modernization of legacy systems and participate in initiatives to unify data platforms across the organization. Required Qualifications
25 years of experience in data engineering or a related field. Proficiency in SQL and moderate experience with Python for data transformation and automation. Hands-on experience with dbt and Snowflake. Experience working with cloud platforms, preferably AWS. Understanding of data modeling and cloud data architecture. Strong analytical and problem-solving skills. Effective communication and collaboration skills in cross-functional teams. Eagerness to learn and grow in a fast-paced, evolving data environment. Preferred Qualifications
Experience with healthcare data, especially revenue cycle management (e.g., 835/837 files). Familiarity with ETL tools, CI/CD workflows, and version control systems (e.g., GitHub). Exposure to data governance and compliance frameworks (e.g., HIPAA).
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Languages
- English
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