Data Modelling & SQL
Global Digital Transformation Leader Team
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Highlights:

5.00 - 8.00 Years
22.00 - 25.00 INR (Lacs)/Yearly
Full-time
Full-time

Roles & Responsibility

  • ata Model Design and Maintenance: Developing and maintaining conceptual, logical, and physical data models that align with business and regulatory requirements.
  • Requirements Gathering: Collaborating with business analysts, data architects, and stakeholders to understand data needs and translate complex requirements into scalable data models.
  • Database Optimization: Optimizing and refining existing data structures and schemas (e.g., Star/Snowflake schema design, Normalization/Denormalization) to enhance database performance, scalability, and data retrieval efficiency.
  • SQL Development and Tuning: Using advanced SQL for data manipulation, complex querying (including CTEs and Window Functions), and performance tuning of database queries and stored procedures.
  • Data Integrity and Quality: Implementing validation rules, constraints, and data governance standards to ensure data consistency, accuracy, and reliability across integrated systems.
  • Documentation: Creating and maintaining comprehensive documentation, including data dictionaries, metadata repositories, and entity-relationship diagrams (ERDs).
  • Collaboration: Working with data engineers, ETL developers, software developers, and data scientists to ensure seamless data flow and integration. 
Required Skills and QualificationsSuccessful candidates for this role typically possess a blend of technical and soft skills: Technical Skills:
  • Proficiency in SQL: Expertise in writing and tuning complex SQL queries.
  • Data Modeling Expertise: Strong understanding of data modeling methodologies and techniques (e.g., ER modeling, dimensional modeling, 3NF).
  • Database Management Systems (DBMS): Experience with relational databases like Oracle, SQL Server, PostgreSQL, MySQL, and potentially NoSQL databases (e.g., MongoDB).
  • Data Modeling Tools: Proficiency in industry-standard tools such as ERwin Data Modeler, SAP PowerDesigner, or Microsoft Visio.
  • Data Warehousing & ETL: Familiarity with data warehousing concepts and ETL (Extract, Transform, Load) processes and tools (e.g., Informatica, Talend).
  • Cloud Platforms (Optional but beneficial): Experience with cloud data platforms like AWS, Azure, or Google Cloud Platform. 

Requirements

  • ata Model Design and Maintenance: Developing and maintaining conceptual, logical, and physical data models that align with business and regulatory requirements.
  • Requirements Gathering: Collaborating with business analysts, data architects, and stakeholders to understand data needs and translate complex requirements into scalable data models.
  • Database Optimization: Optimizing and refining existing data structures and schemas (e.g., Star/Snowflake schema design, Normalization/Denormalization) to enhance database performance, scalability, and data retrieval efficiency.
  • SQL Development and Tuning: Using advanced SQL for data manipulation, complex querying (including CTEs and Window Functions), and performance tuning of database queries and stored procedures.
  • Data Integrity and Quality: Implementing validation rules, constraints, and data governance standards to ensure data consistency, accuracy, and reliability across integrated systems.
  • Documentation: Creating and maintaining comprehensive documentation, including data dictionaries, metadata repositories, and entity-relationship diagrams (ERDs).
  • Collaboration: Working with data engineers, ETL developers, software developers, and data scientists to ensure seamless data flow and integration. 
Required Skills and QualificationsSuccessful candidates for this role typically possess a blend of technical and soft skills: Technical Skills:
  • Proficiency in SQL: Expertise in writing and tuning complex SQL queries.
  • Data Modeling Expertise: Strong understanding of data modeling methodologies and techniques (e.g., ER modeling, dimensional modeling, 3NF).
  • Database Management Systems (DBMS): Experience with relational databases like Oracle, SQL Server, PostgreSQL, MySQL, and potentially NoSQL databases (e.g., MongoDB).
  • Data Modeling Tools: Proficiency in industry-standard tools such as ERwin Data Modeler, SAP PowerDesigner, or Microsoft Visio.
  • Data Warehousing & ETL: Familiarity with data warehousing concepts and ETL (Extract, Transform, Load) processes and tools (e.g., Informatica, Talend).
  • Cloud Platforms (Optional but beneficial): Experience with cloud data platforms like AWS, Azure, or Google Cloud Platform. 

Posted By: Logic Planet It Services