• More than 8 years of experience in various data architecture and engineering roles within data & analytics • Collaborate with stakeholders to understand and document data requirements, business rules, and objectives for the data platform. • Design and develop conceptual, logical, and physical data models that accurately represent the organization's data assets and support its business needs. • Ensure designs meet documented objectives for reliability, scalability, supportability, user experience, security, governance, performance and more • Implement data modeling best practices, including normalization, denormalization, and indexing, to ensure data integrity, performance, and scalability. • Work closely with data engineers and architects to integrate data models into the overall platform architecture, ensuring efficient data processing and storage. • Evaluate and recommend appropriate data storage technologies and database management systems based on project requirements and constraints. • Drive data modeling automation to optimize solutions time to market, increase solutions transparency and overall quality as well as code portability • Collaborate with data analysts to understand analytical requirements and ensure that data models support effective data analysis and reporting. • Communicate effectively with technical and non-technical stakeholders to present and explain data models, design decisions, and recommendations. • Good understanding of key data modeling concepts & patterns related to data systems architecture. • 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. • Implement business and IT data requirements through new data strategies and designs across all data platforms (relational, dimensional, and NoSQL) and data tools (reporting, visualization, analytics, and machine learning). • Work with business and application/solution teams to implement data strategies, build data flows, and develop conceptual/logical/physical data models • Identify the architecture, infrastructure, and interfaces to data sources, tools supporting automated data loads, security concerns, analytic models, and data visualization. • Hands-on modeling, design, configuration, installation, performance tuning, and sandbox POC. • Work proactively and in dependent