Data Modeler - Pune - confidential

    confidential
    confidential Pune

    1 week ago

    Full time
    Description

    Responsibilities:

    • Data Model Design: Design and develop conceptual, logical, and physical data models for transactional systems, data warehouses, data marts, and data lakes. This includes identifying entities, attributes, relationships, hierarchies, and constraints.
    • Requirements Translation: Work closely with business users, data analysts, and data scientists to understand complex business requirements and translate them into effective data models that align with organizational objectives.
    • Database Optimization: Design database schemas that are optimized for data storage, retrieval, and analytical querying, considering performance, scalability, and cost-efficiency.
    • Data Governance & Standards: Apply and enforce data modeling standards, naming conventions, and best practices. Contribute to data governance initiatives, including metadata management, data cataloging, and data lineage documentation.
    • Collaboration: Collaborate with data engineers to ensure smooth implementation of data models in ETL/ELT processes and data pipelines. Work with database administrators (DBAs) to ensure physical models are optimized for specific database technologies.
    • Data Quality & Integrity: Implement data validation rules and integrity constraints within data models to ensure accuracy, consistency, and reliability of data.
    • Documentation: Create and maintain comprehensive data model documentation, data dictionaries, data flow diagrams, and source-to-target mappings.
    • Reverse Engineering & Analysis: Analyze existing database structures and data to identify inconsistencies, redundancies, and opportunities for optimization. Perform reverse engineering of data models where necessary.
    • Technology Evaluation: Research and evaluate new data modeling tools, technologies, and methodologies to improve efficiency and effectiveness of data modeling practices.
    • Problem Solving: Identify and resolve data-related issues, discrepancies, and challenges within the data models and their implementation.

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