Support collaborations with industry partners to understand problem statements and contribute to solution development.
Assist in project scoping by gathering technical inputs and identifying relevant capabilities within the ecosystem (IHLs, research centres, industry partners).
Participate in industry meetings, discussions, and project engagements.
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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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