Looking a Sr Data Modeler/Data Engineer with heavy SQL and strong warehousing experience. In house for data modeling tools they use Erwin or ModelMart. If they have ER Studio that will be an easy transition to learn their tools. Must have strong Oracle and Redshift.
*Talend (ETL) is a plus.
looking for a blend of Data Modeler+Engineer
Skills -
- Strong understanding of data modeling concepts and methodologies (e.g. dimensional, relational, semantic)
- Knowledge of data warehousing design patterns and dimensional modeling techniques
- Extensive experience with data warehousing, OLAP (Online Analytical Processing), big data, and cloud technologies
- Strong proficiency in SQL and NoSQL databases – Oracle, PostgreSQL and Redshift
- Ability to design and implement complex data models to support business requirements that align with reporting and analytics
- Proficiency in SQL and data modeling tools (e.g. ER Studio, ERWin)
- Good understanding of data governance and data management best practices
- Strong ability to analyze and interpret large amounts of data
- Excellent communication skills to effectively collaborate with cross-functional teams and stakeholders
- Ability to stay up to date with new trends and technologies in the data modeling field
- Strong understanding of data architecture concepts, models, and frameworks
- Ability to translate complex technical concepts into business terms
- Knowledge of ETL (Extract, Transform, Load) processes using Talend
- Strong knowledge of data security and privacy best practices
- Understanding of data visualization and reporting tools (e.g. PowerBI, QuickSight)
- Strong problem-solving and critical thinking skills
Job Duties
- Design and implement optimized data models to support business requirements
- Develop and maintain a data dictionary and metadata repository
- Collaborate with cross-functional teams to understand business requirements and translate them into data models
- Ensure data models are accurate, consistent, and support data governance and data management best practices
- Develop and maintain relationships with data owners and stakeholders to ensure data models meet their needs
- Perform data profiling and analysis to understand data quality and completeness
- Ensure data models are optimized for performance and scalability
- Participate in data architecture design and implementation efforts
- Stay up to date with new trends and technologies in the data modeling field
- Document data models and data architecture artifacts
- Collaborate with cross-functional teams, such as software engineers, to integrate data solutions into the overall technology stack
- Troubleshoot and resolve data-related issues and failures in the production environment.
- Build Source-To-Target mapping documents to enable data engineers to build data pipelines
- Implement data security and privacy controls
- Monitor and optimize the performance of data pipelines