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Engineer Data Intelligence/Business Analytics

OSTechnical

  • Irvine, CA
  • 10 days ago

    Highlights

    The Engineer, Data Management and Analytics designs and develops scalable ETL/ELT pipelines leveraging Snowflake, dbt, and Matillion, with a focus on healthcare data (including Epic). Build and maintain ETL/ELT pipelines using Matillion, dbt and Python to ingest and transform data from Epic Clarity, APIs, and third-party systems.

    Numbers & Facts

    LocationIrvine, CA

    Description


    Engineer Data Intelligence/Business Analytics


    The Engineer, Data Management and Analytics designs and develops scalable ETL/ELT pipelines leveraging Snowflake, dbt, and Matillion, with a focus on healthcare data (including Epic). This role incorporates AI-driven techniques to improve data quality, pipeline efficiency, and intelligent data processing.
    1. Partner with Data Warehouse Product Owner and BSA to translate healthcare business requirements (including Epic data) into scalable ETL solutions.
    2. Collaborate with Data Architect to design Snowflake-based data models supporting analytics and AI use case.
    3. Build and maintain ETL/ELT pipelines using Matillion, dbt and Python to ingest and transform data from Epic Clarity, APIs, and third-party systems.
    4. Apply AI-driven techniques for automated data mapping, anomaly detection, and schema drift identification within pipelines.
    5. Develop reusable frameworks for logging, monitoring, and data quality validation using intelligent rules and pattern detection .
    6. Optimize Snowflake performance (partitioning, clustering, cost optimization) for both BI and AI workloads .
    7. Enable feature engineering by preparing curated datasets for downstream machine learning use cases .
    8. Leverage AI-assisted development tools to accelerate SQL/dbt/Python model development and pipeline automation.
    9. Implement data observability practices to proactively identify pipeline failures and data inconsistencies.
    10. Ensure compliance with healthcare data governance standards (HIPAA) and responsible AI usage.
    11. Other duties as assigned.
    REQUIRED:
    • Bachelor's degree or equivalent experience.
    • 2+ years in data engineering with strong ETL/ELT experience.
    • Hands-on experience with Snowflake.
    • Strong SQL skills and experience with dbt or Matillion.
    • Experience building cloud-based data pipelines.
    • Foundational understanding of AI/ML concepts related to data preparation and feature engineering.
    • Experience with Git-based source control and CI/CD pipelines.
    • Experience in data integration from disparate sources: third party vendor data and/or API calls.
    PREFERRED:
    • Experience integrating Epic (Clarity/Caboodle) data models or related healthcare data .
    • Experience in Microsoft SSIS.
    • Experience with Microsoft Fabric
    • Experience using Python for data integration and analysis.
    • Experience with data observability tools and anomaly detection frameworks.
    • Exposure to MLOps practices and model data pipeline support.
    • Experience with Kafka or streaming ingestion frameworks
    • Experience using AI copilots or automation tools in development workflows
    • Experience with Cloud Lakehouse experience for any of the Cloud vendors (AWS, Azure, GCP)
    • Experience with Enterprise orchestration tools like Airflow, Dagster.

    KNOWLEDGE/SKILLS/ABILITIES :
    • Strong problem-solving skills with ability to optimize complex ETL pipelines.
    • Demonstrated excellent interpersonal, verbal and written communication skills.
    • Detail oriented, organized, customer/process focused, problem resolution, and proactive.
    • Ability to draw conclusions and make independent decisions with limited information.
    • Ability to respond to common inquiries from customers, staff, regulatory agencies, vendors, and other members of the business community.
    • Ability to be self-motivated, reliable, work independent, as part of a team, and work effectively with all levels within the organization.
    • Demonstrated intellectual curiosity and willingness to adopt emerging AI technologies.
    • Demonstrated change management in a fast-paced environment under time constraints.
    • Ability to work with mathematical concepts: probability and statistical inference.
    • Ability to effectively simplify complex data and communicate information to leadership.

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