• 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