Data Modeler
Description
Data Modeler – Retail / Finance Domains (One‑Page JD)
Role 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.
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.
Why Ness
Ness offers global, innovative projects across industries, enabling fast career growth. Employees collaborate with highly skilled professionals, work on industry‑leading platforms, and contribute to solutions built on values of rigor, innovation, and partnership.