Enterprise Data Modeler

The Data Modeler owns the design, governance, and evolution of the Bank's enterprise data models across the Silver and Gold layers of Medallion Architecture; translating complex financial source systems into well-governed, reusable, and auditable data structures. This role is the single point of accountability for keeping the enterprise model coherent as multiple projects and vendors propose changes.

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**.**

Key Responsibilities

Enterprise Domain Modeling**: Design and maintain conceptual, logical, and physical data models for the Bank's enterprise data domains — Party/Customer, Account, Product, Transaction, Interactions, Risk, Finance, and Compliance. Manage coordination across project in governing the centralized data model.**

Silver Layer Design: Define Silver Medallion structures (source-aligned with snapshot and SCD Type 2 columns) and Silver Enterprise Domain Model structures in Third Normal Form, including how a single Bronze source table populates multiple target entities. Strong understanding of data dependencies needed.

Historical Data Management: Understand how to classify each entity as SCD Type 1/2, periodic snapshot, fact, etc.; define effective start/end datetime derivation, current flags, delete flags, and change-detection logic, including handling of late-arriving, reprocessed, amended, reversed, and future-dated records.

• Gold Layer / Dimensional Design: Design star schemas, fact tables (transaction, periodic snapshot, accumulating), aggregate tables, and conformed dimensions to serve end consumers.

Keys and Referential Integrity: Define primary, natural, composite, and surrogate keys (e.g., PARTY_SK), foreign-key relationships, cardinality and nullability rules; ensure relationships are only enforced where source data reliably supports them.

Source-to-Target Mapping: Review and validate data mapping sheets from Bronze to Silver to Gold; resolve ambiguities with Systems of Record SMEs and data dictionaries for Banking systems.

• Model Governance and Change Control: Act as owner of the centralized data models; assess impact, approve, and track model change requests raised by delivery teams and vendors, maintain ERDs and version history, and communicate the latest model across projects. Define and maintain lineage from source systems through Bronze, Silver, and Gold.
• Standards Enforcement: Apply and evolve ODS naming conventions, audit column standards, data type standards, and modeling principles; keep the RAID log and design decision records current.
• Domain Placement Decisions: Determine whether a new requirement belongs in an existing domain, warrants extending a domain, or should be handled via transformation or reference data — avoiding models that are over-fitted to a single consuming application.
• Performance-Aware Design: Partner with data engineers to ensure models are fit for Spark/Delta at scale (e.g., avoiding SCD2 treatment of high-volume fact tables, choosing appropriate grain and load strategy).

Stakeholder Collaboration: Work with business units, architects, data engineers, QA, data governance, and external partners to align models with business meaning and regulatory obligations.

Enablement: Mentor data engineers on modeling concepts and support SIT/UAT by clarifying expected model behavior and lineage.

Key Competencies

Systems thinking: Designs for enterprise reuse rather than single-project convenience.

Rigor: Methodical about assumptions, edge cases, and traceability.

Ownership: Proactively governs the model and drives decisions to closure.

Collaboration: Works effectively across internal teams and multiple vendors.

Success Measures (First 12 Months)

• A single, versioned, and published enterprise ODS/ODS model with a working change-control process.
• All in-flight projects mapped to the governed model with documented deviations and rationale.
• Reduced rework from model misclassification or late-discovered relationship gaps.
• Data engineers able to self-serve model references and standards.

Pay: Php120,000.00 - Php170,000.00 per month

Work Location: In person