Data Modeler II

Your Responsibilities

• Implement business and IT data requirements through new data strategies and designs across all data platforms (relational, dimensional, and NoSQL). Work with solution teams and Data Architects to implement data strategies, build data flows, and develop logical/physical data models
• Work with Data Architects to define and govern data modeling and design standards, tools, best practices, and related development for enterprise data models.
• Hands-on modeling, design, configuration, installation, performance tuning, and sandbox POC.
• Work proactively and independently to address project requirements and articulate issues/challenges to reduce project delivery risks.

Your Profile

• Bachelor degree in computer/data science technical or related experience.
• 7+ years of hands-on relational, dimensional, and/or analytic experience (using RDBMS, dimensional, NoSQL data platform technologies, and ETL and data ingestion protocols).
• Experience with data warehouse, Data Lake and enterprise big data platforms in multi-data-center contexts required.
• Good knowledge of metadata management, data modeling, and related tools (Erwin or ER Studio or others) required.
• Preferred experience working with services in Azure / Azure Databricks (Azure Data Factory, Azure Data Lake Storage, Azure Synapse & Azure Databricks). Experience working on SAP Datasphere is a plus.
• Experience in team management, communication, and presentation.
• Understand agile delivery methodology and experience working in scrum environment.
• Understand and translate business needs into data vault and dimensional data models supporting long-term solutions.
• Work with the Application Development team to implement data strategies, create logical and physical data models using best practices to ensure high data quality and reduced redundancy.
• Optimize and update logical and physical data models to support new and existing projects.
• Maintain logical and physical data models along with corresponding metadata.
• Develop best practices for standard naming conventions and coding practices to ensure consistency of data models.
• Recommend opportunities for reuse of data models in new environments.
• Perform reverse engineering of physical data models from databases and SQL scripts.
• Evaluate data models and physical databases for variances and discrepancies.
• Validate business data objects for accuracy and completeness.
• Analyze data-related system integration challenges and propose appropriate solutions.
• Develop data models according to company standards.
• Guide System Analysts, Engineers, Programmers and others on project limitations and capabilities, performance requirements and interfaces.
• Review modifications to existing data models to improve efficiency and performance.
• Examine new application design and recommend corrections if required