Data Modeler
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Required Qualifications
- Experience: At least 5–7 years in data modeling or data architecture, with at least 3 years in banking or financial services.
- Enterprise Scale: Proven experience modeling large enterprise landscapes integrating 40 or more source systems into a single governed enterprise model.
- Data Warehousing: Strong grounding in data warehouse and lakehouse design, including layered (Medallion: Bronze/Silver/Gold) architectures and ODS patterns.
- Modeling Methodologies: Deep, practical knowledge of Inmon (normalized enterprise data warehouse), Kimball (dimensional modeling, bus matrix, conformed dimensions), and Data Vault 2.0 (hubs, links, satellites) — and the judgment to choose between them per layer and use case.
- Normalization: Expert in Third Normal Form (3NF) and normalization/denormalization trade-offs.
- Dimensional Modeling: Expert in star and snowflake schemas, fact and dimension design, grain definition, degenerate and junk dimensions, and slowly changing dimensions (Types 1, 2, and others).
- SCD Type 2: Hands-on design of SCD2 structures, including effective datetime selection from business events vs. processing dates, and point-in-time reconstruction for audit.
- Referential Integrity: Strong command of key strategies, surrogate key generation, orphan handling, and integrity validation across distributed data.
- Financial Data: Working knowledge of banking data — CIF/party, CASA, time deposits, loans, cards, payments (InstaPay, PESONet, SWIFT), treasury deals, fees, and GL postings.
- SQL: Advanced SQL; able to validate models against real data and profile source systems.
- Tools: Experience with a data modeling tool and producing ERDs, data dictionaries, and mapping specifications.
- Communication: Able to explain modeling decisions to both technical and non-technical audiences, and to document decisions clearly.
Preferred Qualifications
- BIAN: Experience applying BIAN (Banking Industry Architecture Network) service domains and business object models to enterprise data domains.
- Platform: Experience with Azure Databricks, Delta Lake, Unity Catalog, ADLS, and PySpark.
- Master Data: Exposure to MDM/customer data mastering, CIF deduplication, and party-role modeling (customer, beneficiary, authorized signatory, employee, affiliate).
- Regulatory: Familiarity with Philippine regulatory and reporting contexts (e.g., BSP, BIR e-invoicing) and data privacy obligations.
- Governance: Experience with business glossaries, data catalogs, metadata management, and data quality frameworks.
- Certifications: CDMP (DAMA), Data Vault 2.0 certification, or Databricks certifications.
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