Data Modeler II

About ADM: We are one of the world’s largest nutrition companies and a global leader in human and animal nutrition. We unlock the power of nature to provide nourishing quality of life by transforming crops into ingredients and solutions for foods, beverages, supplements, livestock, aquaculture, and pets. About ADM India Hub: At ADM, we have long recognized the strength and potential of India’s talent pool, which is why we have maintained a presence in the country for more than 25 years. Building on this foundation, we have now established ADM India Hub, our first GCC in India. At ADM India Hub, we are hiring for IT and finance roles across diverse technology and business functions. We stand at the intersection of global expertise and local excellence, enabling us to drive innovation and support our larger purpose of unlocking the power of nature to enrich quality of life. Data Modeler II Company Introduction: ADM is seeking a Data Modeler who can partner with Solution Leads and Global Process Owners to understand comprehensive business processes, strategy, standards and source systems in order to develop their data and analytical solutions. Communicate Data and Analytics (DnA) capabilities with DNA Leadership, Portfolio Office and Transformation Office to ensure requests are captured and treated in accordance with the IT operating model, demand management processes and delivered in alignment to DNA’s data and application strategies Job Description: The data modeler designs, implements, and documents data architecture and data modeling solutions, which include the use of relational, dimensional, and NoSQL databases. These solutions support enterprise information management, business intelligence, machine learning, data science, and other business interests. The successful candidate will: Be responsible for the development of logical and physical data models across all enterprise data platforms. Oversee and govern the expansion of existing data architecture and the optimization of data query performance via best practices. Demonstrated success in an Agile environment. The candidate must be able to work independently and collaboratively. 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. Skills: Bachelor's degree in computer/data science technical or related experience. 5+ 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/ data analysis, and related tools (Erwin or ER Studio or SAP Power Designer 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.