Data Modelling & SQL

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.