Job Description & How to Apply Below
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 life cycles across multi‑system environments.
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, Power Designer, 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.
#J-18808-Ljbffr
Note that applications are not being accepted from your jurisdiction for this job currently via this jobsite. Candidate preferences are the decision of the Employer or Recruiting Agent, and are controlled by them alone.
To Search, View & Apply for jobs on this site that accept applications from your location or country, tap here to make a Search:
To Search, View & Apply for jobs on this site that accept applications from your location or country, tap here to make a Search:
Search for further Jobs Here:
×