What are the responsibilities and job description for the Data Modeler - Backfill position at Steneral Consulting?
Onsite 3 days a week
Must be local
Candidates need to take a video screen with prime vendor prior to end client submittal
Need valid LinkedIn
% breakdown of what they will be doing
Must be local
Candidates need to take a video screen with prime vendor prior to end client submittal
Need valid LinkedIn
% breakdown of what they will be doing
- Application Modeling, NOT Database Modeling
- OLTP - Transactional Processing System
- Creation of logical, conceptual, and physical data models
- Creation of data dictionary for the attributes in the data model, development, and deployment of data model into the databases.
- Leading the effort in migration of historical data from Rockall into CMS, communicating and coordinating the ETL work with the Data Management teams, creation of predefined reports and dashboards facing Internal customers in Power BI. Number of reports should be finite and pre-defined(20-40).
- See JD – technology agnostic because the application is a file-based system
- Strong understanding of Data Warehouse concepts (ETL, Star Schema, slowly changing dimensions)
- Strong knowledge of relational databases, Data Warehouse, and business Intelligence solutions
- Excellent teamwork and collaboration skills
- Understand existing data model, collaborate with client business & technical teams to develop the target state data model
- Develop Logical, Physical data models and build tables, indexes, database objects
- Creation of logical, conceptual, and physical data models
- Creation of data dictionary for the attributes in the data model, development, and deployment of data model into the databases.
- Leading the effort in migration of historical data from Rockall into CMS, communicating and coordinating the ETL work with the Data Management teams, creation of predefined reports and dashboards facing Internal customers in Power BI. Number of reports should be finite and pre-defined(20-40).
- Strong understanding of Data Warehouse concepts (ETL, Star Schema, slowly changing dimensions)
- Strong knowledge of relational databases, Data Warehouse, and business Intelligence solutions
- Excellent teamwork and collaboration skills
- Understand existing data model, collaborate with client business & technical teams to develop the target state data model
- Develop Logical, Physical data models and build tables, indexes, database objects
- Develop, maintain, and enhance conceptual, logical, and physical data models for various business domains and applications.
- Experience in building Data models in OLTP Systems from the scratch.
- Experience in building generic DATA models for File Processing, Document Processing, Hierarchy models, Data Auditing, Notifications.
- Translate business requirements into data models ensuring structural integrity and compliance with organizational standards.
- Design and optimize database structures to support data storage, retrieval, and analysis.
- Collaborate with database administrators and developers to implement data models in database systems (SQL Server, Oracle).
- Ensure data integrity, security, and performance through proper database design and optimization techniques.
- Perform data profiling, analysis, and validation to identify inconsistencies and anomalies.
- Collaborate with stakeholders to elicit, analyze, and document data requirements.
- Create clear and comprehensive documentation of data models, including entity-relationship diagrams, data dictionaries, and metadata.
- Support data integration projects by mapping data sources to target data models and designing data transformation processes.
- Facilitate data migration efforts, ensuring seamless transition of data from legacy systems to new platforms or environments.
- Work closely with business analysts, developers, and project managers to align data modeling efforts with project timelines and objectives.
- Conduct quality assurance reviews of data models to ensure accuracy, completeness, and usability.
- Identify and resolve data model-related issues, such as performance bottlenecks, data anomalies, and schema conflicts.
- Contribute to the overall success of the team by actively participating in meetings, sharing insights, and contributing innovative ideas.
