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Data Engineer - STUDENT CONVERSION

IBM2,520 open roles

Where
RIO DE JANEIRO, BR
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Your applicationOpen nowData Engineer - STUDENT CONVERSIONIBM · RIO DE JANEIRO, BR

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The clock on this job

Early applications get read.

8.2% of postings close within 7 days. Measured by our own scanner across the market. IBM postings stay open a median of 6 days.

Share of postings closed within
  1. 1.9%1 day
  2. 3.8%3 days
  3. 8.2%7 days
  4. 15.2%14 days
  5. 34.1%30 days
This job: posted today

IBM median: 6 days open

The posting

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences.

1. Data Model Design & Development

  • Design conceptual, logical, and physical data models.
  • Translate business requirements into scalable data structures.
  • Define entities, attributes, relationships, and business rules.
  • Create data models for data warehouses, data lakes, and operational systems.

2. Data Architecture & Integration

  • Collaborate with Data Architects to define enterprise data standards.
  • Design schemas that support data integration across multiple sources.
  • Establish data lineage and data flow mappings.
  • Support modernization and cloud migration initiatives.

3. Database Design

  • Develop normalized and denormalized database models.
  • Design dimensional models (Star Schema and Snowflake Schema).
  • Optimize databases for analytics and transaction processing.
  • Define indexing and partitioning strategies.

4. Data Quality & Governance

  • Establish data standards and naming conventions.
  • Support master data management (MDM) initiatives.
  • Ensure consistency and integrity across enterprise datasets.
  • Identify and resolve data quality issues.

5. Analytics & Reporting Support

  • Design analytical data structures for BI platforms.
  • Enable efficient reporting and dashboard development.
  • Support self-service analytics environments.
  • Partner with Data Engineers and Data Analysts to improve data accessibility.

6. Performance Optimization

  • Analyze database performance bottlenecks.
  • Improve query efficiency and data retrieval performance.
  • Optimize ETL/ELT processes and data pipelines.
  • Implement best practices for large-scale data environments.

7. Documentation & Standards

  • Maintain data dictionaries and metadata repositories.
  • Document data definitions and business terminology.
  • Produce technical design specifications and model documentation.
  • Ensure compliance with enterprise governance requirements.

8. Collaboration

  • Work closely with:Business Analysts Data Engineers Data Architects Database Administrators BI Developers Data Governance Teams AI/ML Engineers

Data Modeling & Architecture

  • Strong understanding of conceptual, logical, and physical data modeling techniques.
  • Experience designing enterprise-scale data architectures.
  • Ability to translate business requirements into data structures and models.
  • Knowledge of data lifecycle management and data governance principles.

Business & Analytical Skills

  • Strong analytical and problem-solving capabilities.
  • Ability to understand complex business processes and map them to data solutions.
  • Experience working with business stakeholders to define data requirements.
  • Understanding of KPI, reporting, and analytics requirements.

Data Governance & Quality

  • Knowledge of data governance frameworks and best practices.
  • Experience with data quality management and data stewardship processes.
  • Understanding of metadata management, data lineage, and master data management (MDM).

Communication & Collaboration

  • Ability to collaborate with Data Architects, Engineers, Analysts, and Business Teams.
  • Strong documentation and presentation skills.
  • Experience participating in Agile/Scrum development environments.
  • Ability to explain technical concepts to non-technical stakeholders.

Required Technical Expertise

Data Modeling

  • Conceptual Data Modeling
  • Logical Data Modeling
  • Physical Data Modeling
  • Entity Relationship (ER) Modeling
  • Dimensional Modeling (Star and Snowflake Schemas)
  • Data Vault Modeling

Database Management

  • Advanced SQL development and optimization
  • Database design and normalization
  • Performance tuning and query optimization
  • Experience with relational and non-relational databases

Data Warehouse & Analytics

  • Data Warehouse design and implementation
  • Fact and Dimension modeling
  • Slowly Changing Dimensions (SCD)
  • Data Mart design
  • OLAP concepts

Data Platforms

Experience with one or more of:

  • Microsoft SQL Server
  • Oracle Database
  • PostgreSQL
  • Snowflake
  • IBM Db2
  • Teradata

Cloud Data Technologies

  • Microsoft Azure Data Services
  • Microsoft Fabric
  • Azure Synapse Analytics
  • Azure Data Factory
  • Azure Databricks
  • AWS Redshift
  • Google BigQuery

Data Integration

  • ETL / ELT frameworks
  • Informatica
  • IBM DataStage
  • SSIS
  • Azure Data Factory
  • Apache Airflow

Programming & Automation

  • SQL (Expert Level)
  • Python
  • PySpark
  • Spark SQL
  • JSON, XML, and API integrations

Data Governance Tools

  • Microsoft Purview
  • Collibra
  • Informatica Data Quality
  • Enterprise Data Catalog solutions

Visualization & Analytics Support

  • Power BI
  • Tableau
  • Cognos Analytics
  • Semantic Models and Data Mart design

• Data Warehousing Knowledge: Exposure to data warehousing concepts and architectures, with an understanding of how data models integrate with data warehouses.

• Advanced Data Modeling: Experience with advanced data modeling techniques, including data governance and data quality, to develop and refine data models that meet business objectives.

• Big Data Technologies: Exposure to big data technologies, including Hadoop and NoSQL databases, with an understanding of how data models can be applied to these technologies.

Uma carreira na IBM Consulting é construída sobre relacionamentos duradouros com clientes e colaboração próxima em todo o mundo. Você trabalhará com empresas líderes em diversos setores, ajudando-as a moldar suas jornadas de nuvem híbrida e IA. Com o suporte de nossos parceiros estratégicos, a robusta tecnologia IBM e a Red Hat, você terá as ferramentas para impulsionar mudanças significativas e acelerar o impacto para os clientes. Na IBM Consulting, a curiosidade impulsiona o sucesso. Você será encorajado a desafiar a norma, explorar novas ideias e criar soluções inovadoras que entregam resultados reais. Nossa cultura de crescimento e empatia foca no seu desenvolvimento de carreira a longo prazo, valorizando suas habilidades e experiências únicas.

Como Engenheiro(a) de Dados especializado(a) em Modelagem de Dados, você projetará e implementará modelos de dados para aplicações e sistemas relacionados, traduzindo as necessidades do negócio em modelos lógicos e físicos de dados. Você aplicará sua expertise em ferramentas e técnicas de modelagem de dados para entregar soluções de alta qualidade.

Suas principais responsabilidades incluirão:

• Projetar Modelos de Dados: Você criará modelos lógicos e físicos de dados que atendam aos requisitos do negócio, alavancando sua expertise em ferramentas e técnicas de modelagem de dados, incluindo ERWin, MDM e/ou ETL.

• Implementar Modelos de Dados: Você traduzirá as necessidades do negócio em modelos de dados, garantindo integração perfeita com aplicações e sistemas relacionados.

• Aplicar Expertise em Modelagem de Dados: Você utilizará seu conhecimento em ferramentas e técnicas de modelagem de dados para desenvolver e aprimorar modelos que atendam aos objetivos do negócio.

• Entregar Soluções: Você colaborará com partes interessadas para entregar soluções de modelagem de dados que atendam aos requisitos e expectativas do negócio.

• Destreza em Ferramentas de Modelagem de Dados: Experiência com ferramentas de modelagem de dados como ERWin, MDM e/ou ETL, com capacidade de aplicar expertise no projeto e implementação de modelos de dados.

• Especialização em Modelos Lógicos e Físicos de Dados: Experiência na criação de modelos lógicos e físicos de dados que atendam aos requisitos do negócio, com conhecimento em técnicas de modelagem de dados e melhores práticas.

• Conhecimento em Tradução de Requisitos de Negócio: Experiência em traduzir necessidades do negócio em modelos de dados, garantindo integração perfeita com aplicações e sistemas relacionados.

• Experiência em Técnicas de Modelagem de Dados: Vivência com diversas técnicas de modelagem de dados, com capacidade de desenvolver e aprimorar modelos que atendam aos objetivos do negócio.

• Familiaridade com Entrega de Soluções: Experiência em trabalhar com partes interessadas para entregar soluções de modelagem de dados que atendam aos requisitos e expectativas do negócio.

• Destreza em Armazenamento de Dados: Experiência com conceitos e arquiteturas de data warehousing, compreendendo como os modelos de dados se integram a esses ambientes.

• Especialização em Modelagem Avançada de Dados: Vivência em técnicas avançadas de modelagem de dados, incluindo governança e qualidade de dados, para desenvolver e aprimorar modelos que atendam aos objetivos do negócio.

• Conhecimento em Tecnologias de Big Data: Experiência com tecnologias de big data, como Hadoop e bancos de dados NoSQL, entendendo como os modelos de dados podem ser aplicados a essas tecnologias.

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