Data Modeling Techniques and Methodologies

Job Description

Project Role : Application Developer Project Role Description : Design, build and configure applications to meet business process and application requirements. Must have skills : Data Modeling Techniques and Methodologies Minimum 5 year(s) of experience is required Educational Qualification : 15 years full time education Summary: As an Application Developer, you will design, build, and configure applications to meet business process and application requirements. You will be responsible for utilizing your expertise in Data Modeling Techniques and Methodologies to develop efficient and effective applications. Your typical day will involve collaborating with the team, analyzing business requirements, designing application solutions, and implementing them to meet the needs of the organization. Roles & Responsibilities: - Expected to be an SME, collaborate and manage the team to perform. - Responsible for team decisions. - Engage with multiple teams and contribute on key decisions. - Provide solutions to problems for their immediate team and across multiple teams. - Design, build, and configure applications based on business process and application requirements. - Analyze business requirements and translate them into technical specifications. - Develop efficient and effective application solutions using Data Modeling Techniques and Methodologies. - Implement and test application solutions to ensure they meet the needs of the organization. Professional & Technical Skills: - Must To Have Skills: Proficiency in Data Modeling Techniques and Methodologies. - Strong understanding of software development principles and methodologies. - Experience in designing and developing applications using [PRIMARY SKILL]. - Knowledge of database management systems and SQL. - Experience with data integration and ETL processes. - Good To Have Skills: Experience with Agile development methodologies. - Familiarity with cloud platforms such as AWS or Azure. - Understanding of data governance and data quality best practices.