Data Modeling Specialist

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.

Responsabilidades e atribuições

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 Responsibilities

Data 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 Design

Azure 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 Qualifications

Education:

• 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 Skills

Data 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 Architecture

• Data Warehousing;
• Lakehouse Architectures;
• Enterprise Information Architecture;
• Reference Data Management;
• Master Data Management Concepts.

Data Governance

• Data 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 Languages

• SQL;
• 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.

Informações adicionais

Modelo de contratação:

• PJ.

Forma de atuação:

• Hibrido (3x por semana presencial no escritório de Pinheiros/SP).