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    Databricks Engineer - Seattle, United States - Intellectt Inc

    Intellectt Inc background
    Insurance
    Description

    Architect Unity Catalog: Provide centralized access control, auditing, lineage, and data discovery capabilities across Databricks workspaces.

    Organize Data Assets: Define and categorize structured and unstructured data within the Unity Catalog.

    Design Azure Cloud-Based Architecture: Implement Data Warehousing and Governance architecture with Lakehouse paradigm.

    Define Access Policies: Ensure secure and consistent access management at a granular level (rows, columns, features) across platforms.

    Leverage Delta Sharing: Enable easy data sharing across regions and platforms.

    Ensure Secure Data Sharing: Share data and AI assets with minimal replication for a unified user experience.

    Integrate Technical Functionality: Ensure data accessibility, accuracy, and security.

    Empower Data Analysts and ETL Engineers: Discover and classify data, notebooks, dashboards, and files across clouds and platforms.

    Implement Single Permission Model: Establish a unified permission model for data and AI assets.

    Monitoring and Observability: Utilize AI for automated monitoring, error diagnosis, and data quality maintenance.

    Set Up Alerts: Configure alerts for Personally Identifiable Information (PII) detection and operational insights.

    Collaborate with Data Scientists and Engineers: Foster adoption of the Unity Catalog among stakeholders.

    Provide Training and Documentation: Ensure effective usage and compliance with governance policies.

    Skills:

    Development and Configuration: Expertise in developing and configuring Unity Catalog.

    Data Governance Principles: In-depth understanding of data governance principles, especially pertaining to data cataloging, access control, lineage, and metadata management.

    Data Warehouse Solutions: Designed data warehouse and data lake solutions, including data processing pipelines using PySpark in Databricks.

    Data Modelling: Experience in Data Modelling on Databricks [Delta Table] for transactional and analytical purposes.

    Data Lake Management: Developed pipelines for loading data into the Data Lake.

    Databricks Proficiency: Proficient in Databricks Platform components such as Databricks SQL, Delta Live Tables, Databricks Repos, and Task Orchestration.

    SQL Skills: Strong SQL skills for data querying and management.

    Data Model Optimization: Ability to design and optimize data models for structured and unstructured data.

    Compute Resource Management: Understanding of managing compute resources, clusters, and workspaces.



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