📍 Job Overview

Job Title: Engenheiro(a) de Dados Cloud
Company: EY
Location: São Paulo, Southeast, Brazil
Job Type: Hybrid (3 days on-site)
Category: Data Engineering
Date Posted: 2025-07-24
Experience Level: Mid-level (2-5 years)
Remote Status: On-site with flexibility for remote work

🚀 Role Summary

As a Cloud Data Engineer at EY, you will be at the forefront of driving data-driven insights and solutions for our clients in the financial sector. This role involves cloud migrations, data ingestion, and process support, enabling you to make a significant impact on our clients' data strategies. By joining our Tech FSO team, you will have the opportunity to work with a diverse and talented group of professionals, fostering a collaborative and innovative environment.

💻 Primary Responsibilities

🔸 Cloud Migration & Data Ingestion

Lead cloud migration projects for our financial sector clients, ensuring data integrity and security throughout the process.
Develop and implement data ingestion pipelines to seamlessly integrate data from various sources into our clients' cloud environments.
Collaborate with cross-functional teams to understand data requirements and ensure data quality and consistency.

🔸 Process Support & Optimization

Analyze and optimize data processes to improve efficiency and performance, reducing manual effort and minimizing errors.
Document and maintain data processes, ensuring knowledge sharing and easy onboarding for new team members.
Troubleshoot data-related issues and provide technical support to internal teams and clients as needed.

🔸 Stakeholder Management & Communication

Effectively communicate complex data concepts and solutions to both technical and non-technical stakeholders.
Collaborate with project managers and other team members to ensure data projects are delivered on time and within budget.
Contribute to the continuous improvement of our data management practices and standards.

🎓 Skills & Qualifications

📚 Education

Bachelor's degree in Statistics, Computer Science, Engineering, or a related field.

🕒 Experience

Proven experience as a Data Engineer, with a strong focus on cloud migrations, data ingestion, and process support.
Experience working with cloud technologies, such as Azure, Databricks, SQL, and Teradata.

🛠 Required Skills

Proficiency in data engineering tools and technologies, such as Apache Spark, Hadoop, and AWS or GCP services.
Strong programming skills in Python, Java, or similar languages.
Excellent problem-solving skills and the ability to work independently or in a team environment.
Strong communication and stakeholder management skills.

🛠 Preferred Skills

Experience with data governance and data quality management.
Familiarity with Agile methodologies and project management tools.
Knowledge of the financial sector and its data landscape.

📊 Web Portfolio & Project Requirements

📐 Portfolio Essentials

A well-structured portfolio showcasing your data engineering projects, with a focus on cloud migrations, data ingestion, and process optimization.
Detailed case studies demonstrating your problem-solving skills and the impact you've made on previous projects.
Live demos or videos highlighting your technical expertise and ability to communicate complex data concepts.

📜 Technical Documentation

Clear and concise documentation of your data engineering processes, including data flows, data transformations, and any relevant code snippets.
Version control and code review practices to ensure code quality and consistency.
Performance testing and optimization strategies to ensure efficient data processing and minimal resource wastage.

💵 Compensation & Benefits

💰 Salary Range

The estimated salary range for this mid-level data engineering role in São Paulo, Brazil, is approximately R$ 8,000 - R$ 12,000 per month, depending on experience and skills. This estimate is based on market research and regional salary standards for data engineering roles in Brazil.

🎁 Benefits

Vale Alimentação (Food Allowance)
Vale Refeição (Meal Allowance)
Vale Transporte (Transportation Allowance)
Plano de Saúde (Health Insurance)
Assistência Odontológica (Dental Insurance)
Universidade Corporativa (Corporate University)
Gympass (Gym Membership)
PLR (Performance-based Bonuses)
Other educational benefits and opportunities for professional development

🕒 Working Hours

Full-time position with flexible remote work options, with an average of 3 days per week in the on-site office.
Working hours: 40 hours per week, with flexible scheduling to accommodate project deadlines and maintenance windows.

🎯 Team & Company Context

🏢 Company Culture

Industry: EY is a global leader in assurance, tax, transaction, and advisory services, with a strong focus on the financial services sector.

Company Size: EY is a large organization with over 350,000 employees globally, providing ample opportunities for career growth and development.

Founded: 1989, as a result of the merger of two major accounting firms, Ernst & Whinney and Arthur Young & Co.

Team Structure:

The Tech FSO team consists of over 4,000 professionals across the Americas, with a strong focus on collaboration and knowledge sharing.
The team is organized into specialized practice groups, including data engineering, data science, and data architecture, among others.
The team works closely with EY's global network of professionals to deliver innovative solutions to clients worldwide.

Development Methodology:

Agile methodologies, including Scrum and Kanban, are used to manage projects and ensure efficient delivery.
EY's global delivery centers and shared service centers support the team's ability to scale and deliver high-quality solutions.
EY's commitment to continuous improvement drives the team's focus on process optimization and innovation.

Company Website: https://www.ey.com/br/pt_br/home

📝 Enhancement Note: EY's strong focus on the financial services sector provides data engineers with unique opportunities to work on complex and impactful projects. The company's global presence and commitment to innovation ensure that data engineers have access to cutting-edge technologies and collaborative work environments.

📈 Career & Growth Analysis

Web Technology Career Level: Mid-level data engineer, responsible for driving data projects and collaborating with cross-functional teams to deliver innovative solutions.

Reporting Structure: The data engineer reports directly to the data engineering practice lead, with regular interactions with project managers, data architects, and other data engineers.

Technical Impact: Data engineers at EY have a significant impact on the company's ability to deliver data-driven insights and solutions to clients. By driving data projects and optimizing data processes, data engineers play a crucial role in EY's success in the financial services sector.

Growth Opportunities:

Technical Progression: With experience and demonstrated expertise, data engineers can progress to senior data engineer or data engineering manager roles, leading teams and driving strategic data initiatives.
Specialization: Data engineers can specialize in specific areas, such as data governance, data architecture, or data science, deepening their expertise and expanding their career prospects.
Global Mobility: EY's global presence provides opportunities for data engineers to work on international projects and gain exposure to diverse markets and cultures.

📝 Enhancement Note: EY's focus on career development and growth opportunities enables data engineers to build successful and fulfilling careers within the organization. The company's commitment to continuous learning and innovation ensures that data engineers have access to the tools and resources they need to excel in their roles and advance their careers.

🌐 Work Environment

Office Type: Modern, collaborative workspaces designed to foster innovation and teamwork.

Office Location(s): EY's São Paulo office is located in the heart of the city, with easy access to public transportation and nearby amenities.

Workspace Context:

Collaboration: EY's open-plan offices encourage collaboration and knowledge sharing among team members.
Workstation: Each data engineer has access to a modern workstation equipped with multiple monitors, high-performance hardware, and specialized software tools.
Meeting Spaces: EY's offices feature dedicated meeting rooms and breakout spaces, providing ample opportunities for team meetings, workshops, and training sessions.

Work Schedule:

Flexible work arrangements, with an average of 3 days per week in the on-site office.
Working hours: 40 hours per week, with flexible scheduling to accommodate project deadlines and maintenance windows.
Remote work options, with secure remote access to EY's internal systems and data platforms.

📝 Enhancement Note: EY's commitment to employee well-being and work-life balance is reflected in the company's flexible work arrangements and modern workspaces. The company's focus on collaboration and innovation ensures that data engineers have access to the resources and support they need to succeed in their roles.

📄 Application & Technical Interview Process

Interview Process:

Online Application Review: EY's recruitment team will review your application and portfolio, assessing your qualifications and fit for the role.
Phone Screen: A brief phone call to discuss your experience, skills, and career goals.
Technical Assessment: A hands-on assessment, focusing on your data engineering skills, problem-solving abilities, and coding proficiency.
Behavioral Interview: An in-depth discussion of your career aspirations, cultural fit, and leadership potential.
Final Decision: EY's recruitment team will make a final decision based on your performance throughout the interview process.

Portfolio Review Tips:

Tailor your portfolio to highlight your cloud migration, data ingestion, and process optimization projects.
Include detailed case studies, demonstrating your problem-solving skills and the impact you've made on previous projects.
Showcase your ability to communicate complex data concepts and solutions to both technical and non-technical stakeholders.

Technical Challenge Preparation:

Brush up on your cloud migration, data ingestion, and process optimization skills, focusing on the technologies and tools relevant to the role.
Practice problem-solving exercises and coding challenges to ensure you're prepared for the technical assessment.
Familiarize yourself with EY's data engineering methodologies, tools, and best practices.

📝 Enhancement Note: EY's comprehensive interview process ensures that candidates have the opportunity to demonstrate their technical skills, problem-solving abilities, and cultural fit. By preparing thoroughly and showcasing your unique qualifications, you'll increase your chances of success in the interview process.

🛠 Technology Stack & Web Infrastructure

Cloud Platforms:

Azure: EY's primary cloud platform, used for data storage, processing, and analysis.
AWS & GCP: Familiarity with these cloud platforms is beneficial, as EY works with clients using these services.

Data Processing & Analysis Tools:

Apache Spark: EY's primary data processing engine, used for data transformation, aggregation, and analysis.
Hadoop: EY's distributed file system, used for data storage and processing at scale.
Python, Java, & SQL: EY's primary programming languages and query language for data manipulation and analysis.

Data Storage & Warehousing:

Azure Data Lake Storage: EY's primary data lake service, used for large-scale data storage and processing.
Azure SQL Data Warehouse & Azure Synapse Analytics: EY's primary data warehousing and business intelligence platforms.
Teradata: Familiarity with this enterprise data warehousing platform is beneficial, as EY works with clients using Teradata.

Data Governance & Quality:

Talend: EY's primary data integration and data quality management tool.
Informatica: Familiarity with this data integration and data quality management tool is beneficial, as EY works with clients using Informatica.

📝 Enhancement Note: EY's technology stack is designed to support data engineers in delivering high-quality, scalable, and efficient data solutions. By gaining proficiency in these tools and technologies, data engineers can drive meaningful impact on EY's clients and contribute to the company's success in the financial services sector.

👥 Team Culture & Values

Data Engineering Values:

Innovation: EY encourages data engineers to explore new technologies, tools, and methodologies to drive continuous improvement.
Collaboration: EY fosters a culture of teamwork and knowledge sharing, with regular opportunities for cross-functional collaboration and learning.
Quality: EY is committed to delivering high-quality, reliable, and efficient data solutions to clients.
Integrity: EY expects data engineers to uphold the highest ethical standards in their work and interactions with clients and colleagues.

Collaboration Style:

Cross-functional Teams: Data engineers work closely with data architects, data scientists, and other specialists to deliver integrated data solutions.
Agile Methodologies: EY uses Agile methodologies, including Scrum and Kanban, to manage projects and ensure efficient delivery.
Knowledge Sharing: EY encourages data engineers to share their expertise and learn from their colleagues, fostering a culture of continuous learning and improvement.

📝 Enhancement Note: EY's data engineering team culture is built on a foundation of innovation, collaboration, and continuous learning. By embracing these values and working closely with colleagues, data engineers can drive meaningful impact on EY's clients and contribute to the company's success in the financial services sector.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Cloud Migration Complexities: Data engineers may face challenges in migrating complex data environments to the cloud, requiring deep technical expertise and problem-solving skills.
Data Ingestion & Quality: Data engineers must ensure that data is ingested accurately and efficiently, maintaining data quality and consistency throughout the process.
Process Optimization: Data engineers must continuously optimize data processes to improve efficiency, reduce manual effort, and minimize errors.
Stakeholder Management: Data engineers must effectively communicate complex data concepts and solutions to both technical and non-technical stakeholders, requiring strong communication and interpersonal skills.

Learning & Development Opportunities:

Technical Training: EY offers comprehensive training programs, including online courses, workshops, and certifications, to help data engineers develop their skills and advance their careers.
Mentoring & Coaching: EY provides mentoring and coaching opportunities, pairing data engineers with experienced colleagues who can offer guidance and support.
Conferences & Events: EY encourages data engineers to attend industry conferences and events, providing opportunities to learn from thought leaders and network with peers.

📝 Enhancement Note: EY's commitment to learning and development ensures that data engineers have access to the tools and resources they need to succeed in their roles and advance their careers. By embracing these opportunities and continuously expanding their skills and knowledge, data engineers can drive meaningful impact on EY's clients and contribute to the company's success in the financial services sector.

💡 Interview Preparation

Technical Questions:

Cloud Migration: Describe your experience with cloud migration projects, including data migration strategies, data validation, and data governance considerations.
Data Ingestion: Explain your approach to data ingestion, including data profiling, data cleansing, and data transformation techniques.
Process Optimization: Discuss your experience with process optimization, including process mapping, process automation, and process improvement methodologies.
Stakeholder Management: Describe your experience working with stakeholders, including communication strategies, conflict resolution, and change management techniques.

Company & Culture Questions:

EY's Data Engineering Practice: Explain what you understand about EY's data engineering practice and how you can contribute to its success.
Data Engineering Methodologies: Describe your experience with data engineering methodologies, including Agile, Scrum, and Kanban.
Data Governance & Quality: Discuss your understanding of data governance and data quality management, including data lineage, data cataloging, and data access control.

Portfolio Presentation Strategy:

Cloud Migration Projects: Highlight your cloud migration projects, demonstrating your ability to migrate complex data environments to the cloud efficiently and securely.
Data Ingestion & Quality: Showcase your data ingestion and data quality management projects, demonstrating your ability to ensure data accuracy, completeness, and consistency.
Process Optimization: Present your process optimization projects, highlighting your ability to improve efficiency, reduce manual effort, and minimize errors.
Stakeholder Management: Demonstrate your ability to communicate complex data concepts and solutions to both technical and non-technical stakeholders, using real-world examples and case studies.

📝 Enhancement Note: EY's interview process is designed to assess your technical skills, problem-solving abilities, and cultural fit. By preparing thoroughly and showcasing your unique qualifications, you'll increase your chances of success in the interview process.

📌 Application Steps

To apply for this Cloud Data Engineer position at EY:

Tailor Your Portfolio: Highlight your cloud migration, data ingestion, and process optimization projects, emphasizing your problem-solving skills and the impact you've made on previous projects.
Optimize Your Resume: Focus on your data engineering experience, skills, and achievements, using relevant keywords and industry-specific examples to demonstrate your qualifications.
Prepare for Technical Challenges: Brush up on your cloud migration, data ingestion, and process optimization skills, focusing on the technologies and tools relevant to the role. Practice problem-solving exercises and coding challenges to ensure you're prepared for the technical assessment.
Research EY: Familiarize yourself with EY's data engineering practice, methodologies, and company culture. Prepare thoughtful questions to ask during the interview process, demonstrating your interest in the role and the company.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and web technology industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

Engenheiro (a) de Dados Cloud