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Gupy

Data modeling specialist (4815)

Logo Keep Simple

Empresa

Keep Simple

Onde estamos

São Paulo / SP

Vagas da empresa

Nível

Não informado

Período

Híbrido

Tipo

PJ

Salário

A combinar

Responsabilidades e atribuições

Come work for a large global financial and insurance products company!

This is your chance !!

Start a successful career in a renowned company in the international market!

Great opportunity!

Global insurance and asset management company seeks a responsible, organized, dynamic and team-oriented person

  • We are seeking an experienced Data Modeling Specialist to design, develop, and maintain enterprise data models that support modern analytics, reporting, AI, and operational data solutions
  • This role will be responsible for creating and governing conceptual, logical, and physical data models across cloud and on-premise platforms, including Azure Synapse Analytics, Oracle, and Databricks
  • The ideal candidate will possess deep expertise in data architecture, data modeling standards, metadata management, data lineage, and governance practices
  • The role will partner closely with Data Engineers, Solution Architects, Business Analysts, Governance teams, and Data Product Owners to ensure consistent, scalable, and business-aligned data designs
  • Key ResponsibilitiesData Modeling:Design and maintain enterprise conceptual, logical, and physical data models
  • Develop dimensional, normalized, and hybrid data models supporting analytics and operational workloads
  • Translate business requirements into scalable and maintainable data structures
  • Create subject area models, canonical data models, and data domain structures
  • Ensure alignment of data models with enterprise architecture and business capabilities
  • Platform-Specific Data DesignAzure Synapse Analytics:Design dimensional and reporting models optimized for Synapse dedicated and serverless environments
  • Define partitioning, distribution, and performance optimization strategies
  • Support data warehouse and lakehouse architectures
  • Databricks:Model data structures supporting Lakehouse architectures
  • Design Bronze, Silver, and Gold layer data models
  • Support Delta Lake implementations and data product development
  • Collaborate with Data Engineering teams to optimize model performance and scalability
  • Oracle:Design and maintain normalized operational data models
  • Support transactional and analytical databases
  • Define indexing, partitioning, and performance optimization standards
  • Collaborate with database administrators to ensure efficient physical database implementations
  • Data Mapping and Integration:Create and maintain source-to-target mapping documentation
  • Define transformation rules and business logic for data integration solutions
  • Support data migration, modernization, and cloud transformation initiatives
  • Work with Data Engineers to ensure accurate implementation of data models and mappings
  • Validate data integrity across inbound and outbound interfaces
  • Data Governance and Metadata Management:Support enterprise data governance initiatives
  • Define and maintain business and technical metadata
  • Establish and document data standards and naming conventions
  • Support data stewardship activities and governance reviews
  • Ensure compliance with enterprise data management policies and regulatory requirements
  • Data Lineage and Data Quality:Document end-to-end data lineage from source systems through consumption layers
  • Support implementation of lineage management tools and processes
  • Partner with governance teams to improve data transparency and traceability
  • Define data quality rules, standards, and validation requirements
  • Assist in root-cause analysis of data quality issues
  • Collaboration and Leadership:Partner with Business Analysts, Data Architects, Data Engineers, and Solution Architects
  • Participate in architecture reviews and solution design workshops
  • Provide guidance on data modeling best practices and standards
  • Mentor junior team members in data architecture and modeling disciplines
  • Promote data management and governance best practices across the organization

Requisitos e qualificações

  • Required QualificationsEducation:Bachelor's degree in Computer Science, Information Systems, Data Management, Engineering, or a related field
  • Master's degree preferred
  • Experience:7+ years of experience in data modeling, data architecture, or enterprise data management
  • Proven experience creating logical and physical data models for large-scale data platforms
  • Experience supporting data warehouse, database, and lakehouse environments
  • Experience with enterprise data governance and metadata management initiatives
  • Required Technical SkillsData Modeling:Conceptual Data Modeling
  • Logical Data Modeling
  • Physical Data Modeling
  • Dimensional Modeling (Star and Snowflake Schemas)
  • Third Normal Form (3NF)
  • Data Vault Modeling
  • Canonical Data Modeling
  • Data Platforms:Azure Synapse Analytics
  • Databricks Lakehouse Platform
  • Oracle Database
  • SQL Server (Preferred)
  • Data ArchitectureData Warehousing
  • Lakehouse Architectures
  • Enterprise Information Architecture
  • Reference Data Management
  • Master Data Management Concepts
  • Data GovernanceData Lineage
  • Metadata Management
  • Data Catalog Solutions
  • Data Stewardship
  • Data Quality Management
  • Business Glossary Development
  • Data Integration:Source-to-Target Mapping
  • Data Transformation Design
  • ETL / ELT Processes
  • Integration Architecture
  • Tools (Preferred):Erwin Data Modeler
  • ER/Studio
  • SAP PowerDesigner
  • Microsoft Purview
  • Collibra
  • Informatica Metadata Manage
  • Technical LanguagesSQL
  • PL/SQL
  • Python (Preferred)
  • Preferred Qualifications:Experience with Microsoft Purview or similar metadata and governance tools
  • Experience supporting cloud modernization initiatives
  • Knowledge of Databricks Unity Catalog and governance capabilities
  • Understanding of AI, analytics, and machine learning data requirements
  • Experience within Insurance, Financial Services, or other regulated industries
  • Familiarity with DataOps and DevOps practices
  • Key Competencies:Enterprise Data Architecture
  • Data Modeling Expertise
  • Business Analysis
  • Information Governance
  • Analytical Thinking
  • Documentation and Communication
  • Stakeholder Management
  • Problem Solving
  • Attention to Detail
  • Collaboration and Leadership
  • Success Measures:High-quality, reusable, and scalable data models delivered across data platforms
  • Accurate and complete source-to-target mappings
  • Comprehensive data lineage documentation
  • Improved data governance and metadata management maturity
  • Increased consistency of enterprise data assets and standards
  • Successful support of analytics, reporting, AI, and regulatory reporting initiatives
  • Fluent english
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