Senior Data Modeler – (OLTP & SQL Server)

Accountabilities:: Design conceptual, logical, and physical data models for enterprise OLTP applications, ensuring structures are scalable, maintainable, and aligned with application requirements. Translate business requirements into well-defined entities, attributes, relationships, cardinalities, and business rules that accurately represent business processes. Develop ER models and detailed database designs covering tables, primary keys, foreign keys, constraints, indexes, and other relational components. Apply normalization principles, including 3NF, to create efficient relational structures while maintaining appropriate data integrity and minimizing unnecessary duplication. Work closely with Architects, Developers, Business Analysts, Product Managers, and other stakeholders to ensure database designs support functional and technical requirements. Review and optimize existing database structures to improve data integrity, scalability, maintainability, and performance as applications and business needs evolve. Support database enhancements, new application features, and changes to transactional systems by evaluating their impact on existing data models and structures. Apply a strong understanding of transactional processing, concurrency, relationships, and referential integrity when designing and reviewing database solutions. Document database architectures, design decisions, assumptions, dependencies, business rules, and relevant technical considerations to support development and long-term maintenance. Use AI-assisted tools such as ChatGPT, Claude, or GitHub Copilot where appropriate to improve SQL development, documentation, troubleshooting, analysis, and overall engineering productivity. Requirements Bring 8–10 years of overall experience in database development, including at least 3 years of hands-on experience in data modeling or database design. Demonstrate strong practical expertise with Microsoft SQL Server and T-SQL, including the ability to design and work with complex relational database structures. Have a strong understanding of OLTP architectures and transactional database environments, including transaction processing, concurrency, and referential integrity. Possess hands-on experience creating logical and physical data models for enterprise or commercial applications. Demonstrate strong knowledge of ER modeling, normalization and 3NF, primary and foreign keys, cardinality, constraints, referential integrity, and indexing strategies. Be able to translate complex business requirements into effective database structures and communicate design decisions clearly to both technical and non-technical stakeholders. Bring strong analytical and problem-solving skills, with the ability to identify data-modeling challenges and develop practical, scalable solutions. Demonstrate strong communication and collaboration skills and the ability to work effectively with architects, developers, product teams, and business stakeholders. Experience with Azure SQL or AWS RDS, database performance tuning, transaction and concurrency concepts, audit/history data design, or API and application integration is a plus. Experience in healthcare, pharmaceutical, or life sciences environments is advantageous, as is familiarity with AI-assisted tools for engineering productivity. Benefits Opportunity to work on enterprise-grade database architecture and transactional systems with significant business impact. Deep exposure to Microsoft SQL Server, T-SQL, OLTP architectures, relational modeling, and large-scale application data structures. Cross-functional collaboration with Business Analysts, Product Managers, Architects, Developers, and other technology professionals. Opportunity to influence database architecture from requirements gathering through conceptual, logical, and physical design. Exposure to modern cloud database technologies such as Azure SQL and AWS RDS. Opportunity to apply AI-assisted development tools to SQL development, documentation, troubleshooting, and productivity. Potential exposure to healthcare, pharmaceutical, and life sciences data environments. Environment that values continuous learning, innovation, collaboration, transparent communication, and professional development. Opportunity to contribute to technology solutions supporting products and services with a meaningful impact on patients and healthcare outcomes.