Build and maintain efficient data models usingstar schema, snowflake schema, or normalized structures.
Develop set analysis expressions, mastermeasures, dimensions, variables, filters, drill-downs, and reusable dashboardcomponents.
Perform data analysis, source-to-target mapping,validation, and reconciliation, and use SQL queries to investigate datadiscrepancies and troubleshoot issues.
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Provide advanced support and troubleshooting for data pipelines, ETL/ELT processes, and data integration workflows across cloud and on-prem environments.
Enhance and mentor others in organizational, communication, and analytical skills, fostering a collaborative and data-driven engineering culture.
Gain exposure to the end-to-end machine learning lifecycle, from experimentation to production, by enabling robust and scalable data access for model training and inference.
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Quality Assurance & Testing: Prepare test cases, execute data quality validations, identify anomalies, and support bug resolution.
Technical Documentation: Author and maintain clear project documentation, data mapping specs, and system guides.
Deployment & Knowledge Transfer: Support system deployments, assist in preparing user training materials, and participate in client onboarding activities.
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Quality Assurance & Testing: Prepare test cases, execute data quality validations, identify anomalies, and support bug resolution.
Technical Documentation: Author and maintain clear project documentation, data mapping specs, and system guides.
Deployment & Knowledge Transfer: Support system deployments, assist in preparing user training materials, and participate in client onboarding activities.
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