Job descriptionRole Overview Seeking an experienced Data Modeler with strong Retail (preferably Loyalty) and Finance/Capital Markets domain knowledge. The role focuses on designing scalable, consistent, and extensible data models that support complex operational lifecycles across multi‑system environments. Key Responsibilities Data Modeling & Architecture - Design conceptual, logical, and physical data models across Retail, Supply Chain, Banking, and Capital Markets domains. - Model time‑series, reference, market, and transactional data. - Align designs with Medallion Architecture (Bronze/Silver/Gold) and cloud lakehouse environments (AWS, Spark, Parquet, Iceberg). Standards & Governance - Define modeling standards, templates, naming conventions, and data dictionaries. - Establish best practices to ensure consistency, scalability, and long‑term extensibility. Model Review & Optimization - Evaluate existing data models for alignment with best practices. - Identify gaps, inconsistencies, and improvement areas for multi‑system integration. Lifecycle‑Wide Modeling Support - Setup: Introduce new attributes for segmentation, eligibility, rules, and workflow triggers. - Execution: Model structures supporting multi‑step workflows, state transitions, and real‑time/near‑real‑time flows. - Financial Processing: Define data required for funding logic, allocation rules, settlement, and reconciliation. - Analytics: Build scalable facts, dimensions, and hierarchies for performance measurement and insights. Collaboration - Work with business SMEs, architects, engineering, finance, and analytics teams. - Translate business rules into normalized, logical, and physical models. Required Skills & Experience - 6–12 years of enterprise data modeling experience in complex, multi‑system environments. - Strong expertise in ERwin, ER/Studio, PowerDesigner, or similar tools. - Proficient in relational, dimensional, and lakehouse modeling. - Hands‑on experience with cloud storage formats (Parquet/Iceberg) and distributed computing platforms. - Solid understanding of Finance & Capital Markets data (trades, risk, positions, reference data). - Strong communication, analytical, and documentation skills. Preferred Qualifications - Exposure to Azure, Databricks, Snowflake, DBT. - Knowledge of data governance, lineage, and regulatory compliance. - Experience working in Agile/Scrum environments.