📍 Job Overview

Job Title: Cloud Data Architect
Company: Capgemini
Location: Montréal, Quebec, Canada
Job Type: On-site
Category: Data Architecture
Date Posted: 2025-06-18
Experience Level: 10+
Remote Status: On-site

🚀 Role Summary

Key Responsibilities: Design and implement comprehensive data architecture strategies, collaborate with stakeholders, and ensure data quality and governance.
Key Skills: Data Warehousing, Cloud Data Platforms, ELT Processes, Data Integration Techniques, Snowflake, Databricks, Spark, SQL, Data Modeling, Python, Big Data Technologies, Cloud Services, DevOps Practices, Analytical Skills, Problem-Solving Skills, Communication Skills.

📝 Enhancement Note: This role requires a strong background in data warehousing and cloud data platforms, with a focus on Snowflake, Databricks, and Spark. Candidates should have expertise in data modeling, SQL, and big data technologies to succeed in this position.

💻 Primary Responsibilities

Data Architecture Design: Develop and implement comprehensive data architecture strategies that support the needs of the Data on Snowflake Cloud Architecture. Design scalable data models that facilitate efficient data procurement, storage, processing, and analysis.
Data Modeling: Create logical and physical data models that reflect business data consumption needs. Ensure data models support data mining, business intelligence, and analytics activities and AI tools. Develop semantic models to facilitate self-service operations.
Data Governance and Quality: Help establish and facilitate management of data definitions, standards, policies, and procedures. Enhance data quality by setting up frameworks for data consistency, accuracy, and completeness. Lead efforts in data cataloging for improved data discovery and understanding.
Collaboration: Work closely with data engineers, analysts, product owners, and other stakeholders to deliver data products that align with business objectives. Facilitate cross-functional team efforts to ensure data architecture supports all aspects of the business.
Tool Utilization and Expertise: Utilize advanced data modeling tools to design and optimize data architectures. Stay updated with the latest trends in data technology and methodologies applicable to asset management. Have familiarity with business intelligence tool set ecosystem and strong experience with some.

📝 Enhancement Note: This role requires a deep understanding of data warehousing concepts, data modeling, and data integration techniques. Candidates should be proficient in SQL, Python, and big data technologies to excel in these responsibilities.

🎓 Skills & Qualifications

Education: A Bachelor's degree in Computer Science, Information Technology, or a related field is typically required for this role. A Master's degree in a relevant field may be beneficial.

Experience: Candidates should have 7-12 years of experience in Data Warehousing, including at least 3+ years with cloud data platforms. Proven expertise in data warehousing solutions, ELT processes, and data integration techniques is essential.

Required Skills:

Proven expertise in data warehousing solutions, ELT processes, and data integration techniques.
Strong experience with Snowflake Cloud database; hands-on with Databricks and Spark.
Expert-level SQL skills and experience with relational databases (e.g., Snowflake, Teradata, PostgreSQL, Sybase, DB2).
Proficiency in data modeling and tools like Erwin, Power Designer, or equivalents.
Solid understanding of Data Warehousing concepts (data modeling, transformations).
Experience developing and supporting data ingestion frameworks using SQL, Spark, Python, Databricks, Snowpipe, etc.
Familiarity with big data technologies and cloud services (AWS, Azure, Google Cloud).
Good programming skills (Python preferred) and Unix shell scripting knowledge.
Understanding of DevOps practices in the data space.
Strong analytical, problem-solving, and communication skills.
Ability to work effectively in collaborative, agile environments.

Preferred Skills:

Experience with data governance and quality frameworks.
Knowledge of data cataloging tools and techniques.
Familiarity with business intelligence tools and self-service analytics platforms.
Experience with data visualization and reporting tools.
Knowledge of data privacy regulations and data protection best practices.

📝 Enhancement Note: While the required skills list is comprehensive, candidates should also possess a strong understanding of data governance, data quality, and data cataloging to excel in this role. Familiarity with business intelligence tools and self-service analytics platforms is also beneficial.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

Data Warehouse Design: Include examples of data warehouse designs, data models, and data flows that demonstrate your understanding of data warehousing concepts and best practices.
Data Integration Projects: Showcase projects that involve data integration, ETL processes, and data ingestion frameworks. Highlight your proficiency in SQL, Spark, Python, and other relevant tools.
Data Governance & Quality: Provide examples of data governance frameworks, data quality initiatives, and data cataloging efforts. Demonstrate your understanding of data governance, data quality, and data cataloging principles.
Cloud Data Platform Projects: Include projects that showcase your experience with cloud data platforms, such as Snowflake, Databricks, and Spark. Highlight your ability to design, implement, and manage data architectures in the cloud.

Technical Documentation:

Data Warehouse Documentation: Provide detailed documentation of your data warehouse designs, including data models, data flows, and data lineage. Explain the rationale behind your design choices and how they support business objectives.
ETL Processes & Data Ingestion: Document your ETL processes and data ingestion frameworks, including data sources, data transformations, and data targets. Explain how these processes ensure data quality, consistency, and accuracy.
Data Governance & Quality Documentation: Document your data governance and quality initiatives, including data policies, data standards, and data quality metrics. Explain how these initiatives ensure data accuracy, completeness, and consistency.
Cloud Data Platform Documentation: Document your cloud data platform projects, including data architecture designs, data processing workflows, and data management strategies. Explain how these projects leverage cloud technologies to support business objectives.

📝 Enhancement Note: While not explicitly stated, it is essential to provide clear, concise, and well-organized documentation that demonstrates your technical expertise and attention to detail. Include visual representations, such as data flow diagrams and entity-relationship diagrams, to enhance understanding.

💵 Compensation & Benefits

Salary Range: The salary range for this role is CAD 120,000 - CAD 160,000 per year, based on market research and industry standards for experienced data architects in the Montreal area.

Benefits:

Comprehensive health and dental insurance plans.
Retirement savings plans, including a company-matched pension plan.
Employee stock purchase plan.
Generous vacation and time-off policies.
Professional development opportunities, including training, certifications, and conference attendance.
Employee assistance programs, including counseling services and work-life balance resources.
Flexible work arrangements, including remote work and flexible hours.

Working Hours: The standard workweek is 37.5 hours, Monday to Friday, with some flexibility for remote work and flexible hours. Occasional overtime may be required to meet project deadlines or address maintenance issues.

📝 Enhancement Note: The salary range provided is an estimate based on market research and industry standards. Actual compensation may vary depending on factors such as experience, skills, and performance. Benefits are subject to change and may vary depending on the employee's location and employment status.

🎯 Team & Company Context

🏢 Company Culture

Industry: Capgemini is a global leader in consulting, technology services, and digital transformation. The company operates in various industries, including finance, healthcare, retail, and public sector, providing a diverse range of clients and projects for data architects to work on.

Company Size: Capgemini is a large corporation with over 270,000 employees worldwide, providing ample opportunities for career growth and professional development. The Montreal office is one of Capgemini's many locations, offering a collaborative and multicultural work environment.

Founded: Capgemini was founded in 1967 and has since grown into a global leader in technology and consulting services. The company has a strong commitment to innovation, sustainability, and social responsibility.

Team Structure: The data architecture team at Capgemini Montreal works closely with data engineers, data analysts, and other stakeholders to deliver data products that align with business objectives. The team is structured to support cross-functional collaboration and ensure data architecture supports all aspects of the business.

Development Methodology: Capgemini employs Agile methodologies, such as Scrum and Kanban, to manage projects and ensure efficient delivery of data products. The company emphasizes collaboration, continuous improvement, and customer satisfaction in its development processes.

Company Website: https://www.capgemini.com/

📝 Enhancement Note: Capgemini's global presence and diverse client base offer data architects the opportunity to work on complex and challenging projects across various industries. The company's commitment to innovation, sustainability, and social responsibility provides a supportive environment for professional growth and development.

📈 Career & Growth Analysis

Data Architecture Career Level: This role is at the senior level, requiring a deep understanding of data warehousing, data modeling, and data integration techniques. The successful candidate will have at least 7-12 years of experience in data warehousing, including at least 3+ years with cloud data platforms.

Reporting Structure: The Cloud Data Architect reports directly to the Data Architecture Manager and works closely with data engineers, data analysts, and other stakeholders to deliver data products that align with business objectives.

Technical Impact: The Cloud Data Architect plays a critical role in designing and implementing data architectures that support the needs of the business. The successful candidate will have a significant impact on data quality, data governance, and data-driven decision-making within the organization.

Growth Opportunities:

Technical Growth: Capgemini offers opportunities for technical specialization and certification in emerging technologies, such as cloud data platforms, big data technologies, and data governance tools.
Leadership Growth: With experience and demonstrated success, data architects can progress to management roles, overseeing teams and driving strategic initiatives in data architecture and data management.
Cross-Functional Growth: Data architects can expand their skills and expertise by working on cross-functional projects with teams from other disciplines, such as software development, business intelligence, and data analytics.

📝 Enhancement Note: Capgemini's large and diverse client base, along with its commitment to innovation and professional development, provides ample opportunities for career growth and advancement in data architecture. The company's global presence and collaborative work environment foster a supportive and challenging environment for data professionals to excel.

🌐 Work Environment

Office Type: Capgemini Montreal operates in a modern, open-concept office space designed to foster collaboration and innovation. The office features state-of-the-art technology, comfortable workspaces, and amenities to support employee productivity and well-being.

Office Location(s): Capgemini Montreal is located in the heart of downtown Montreal, with easy access to public transportation and nearby amenities. The office is accessible and inclusive, with features such as wheelchair access, gender-neutral restrooms, and lactation rooms.

Workspace Context:

Collaborative Workspace: The open-concept office design encourages collaboration and communication among team members, with ample space for team meetings, brainstorming sessions, and informal discussions.
Technology & Amenities: Capgemini Montreal provides employees with access to modern technology, including high-speed internet, powerful workstations, and specialized software tools. The office also features amenities such as a fully-equipped kitchen, relaxation areas, and fitness facilities.
Work-Life Balance: Capgemini Montreal offers flexible work arrangements, including remote work and flexible hours, to support employees' work-life balance. The company also provides resources and support for employees' physical and mental well-being, such as employee assistance programs and wellness initiatives.

Work Schedule: The standard workweek is 37.5 hours, Monday to Friday, with some flexibility for remote work and flexible hours. Occasional overtime may be required to meet project deadlines or address maintenance issues.

📝 Enhancement Note: Capgemini Montreal's modern, collaborative work environment, along with its commitment to work-life balance and employee well-being, creates a supportive and productive atmosphere for data architects to thrive. The office's central location and accessibility make it an attractive option for professionals living and working in the Montreal area.

📄 Application & Technical Interview Process

Interview Process:

Phone/Video Screen: A brief phone or video call to assess communication skills, cultural fit, and basic technical knowledge.
Technical Assessment: A hands-on technical assessment, focusing on data warehousing, data modeling, and data integration techniques. The assessment may include tasks such as designing data models, optimizing ETL processes, or developing data ingestion frameworks.
Behavioral Interview: A structured interview focused on behavioral questions, assessing problem-solving skills, analytical thinking, and communication abilities.
Final Interview: A final interview with senior leadership to discuss the candidate's fit for the role, career aspirations, and cultural alignment.

Portfolio Review Tips:

Data Warehouse Design: Highlight your expertise in data warehousing solutions, ELT processes, and data integration techniques by showcasing your data warehouse designs, data models, and data flows.
Data Governance & Quality: Demonstrate your understanding of data governance, data quality, and data cataloging principles by providing examples of data governance frameworks, data quality initiatives, and data cataloging efforts.
Cloud Data Platform Projects: Showcase your experience with cloud data platforms, such as Snowflake, Databricks, and Spark, by including projects that highlight your ability to design, implement, and manage data architectures in the cloud.
Technical Documentation: Provide clear, concise, and well-organized documentation that demonstrates your technical expertise and attention to detail. Include visual representations, such as data flow diagrams and entity-relationship diagrams, to enhance understanding.

Technical Challenge Preparation:

Data Warehouse Design: Brush up on your data warehousing concepts, data modeling techniques, and data integration strategies. Familiarize yourself with industry best practices and relevant tools, such as Erwin, Power Designer, or equivalents.
Data Governance & Quality: Review data governance frameworks, data quality initiatives, and data cataloging principles. Familiarize yourself with relevant tools and technologies, such as data governance platforms, data quality tools, and data cataloging tools.
Cloud Data Platforms: Deepen your understanding of cloud data platforms, such as Snowflake, Databricks, and Spark. Familiarize yourself with cloud data platform-specific best practices, such as data partitioning, data compression, and data caching.

ATS Keywords: [Comprehensive list of data architecture, data warehousing, and cloud data platform-relevant keywords for resume optimization, organized by category: data warehousing techniques, data integration tools, cloud data platforms, big data technologies, data governance principles, data quality tools, and industry terms]

📝 Enhancement Note: Capgemini's structured interview process and comprehensive assessment of technical skills, problem-solving abilities, and cultural fit ensure that candidates have a clear understanding of the role's requirements and expectations. By preparing thoroughly and showcasing your expertise in data warehousing, data modeling, and data integration techniques, you can demonstrate your qualifications for the Cloud Data Architect position.

🛠 Technology Stack & Web Infrastructure

Data Warehousing & ETL Tools:

Snowflake: A cloud-based data warehousing platform that provides a scalable, secure, and cost-effective solution for data storage and processing.
Databricks: A unified data analytics platform that combines data science, engineering, and business analytics. Databricks provides a collaborative workspace for data teams to perform data preparation, data engineering, data science, and machine learning tasks.
Spark: A fast and general engine for large-scale data processing. Spark provides high-level APIs for data processing, machine learning, and graph computation. It runs on top of Apache Hadoop and can process data in Hadoop Distributed File System (HDFS), Amazon S3, and other data sources.
Talend: An open-source data integration and data management platform that provides a wide range of tools for data extraction, transformation, and loading (ETL) processes. Talend supports data integration, data quality, and data governance initiatives.

Cloud Data Platforms:

AWS: Amazon Web Services provides a comprehensive, evolving cloud computing platform that includes a mix of Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS) offerings. AWS supports a wide range of use cases, including data warehousing, data lakes, and data analytics.
Azure: Microsoft Azure is a comprehensive cloud platform that offers a wide range of services, including data warehousing, data lakes, and data analytics. Azure supports a mix of IaaS, PaaS, and SaaS offerings and integrates with Microsoft's on-premises products, such as SQL Server and Analysis Services.
Google Cloud: Google Cloud Platform provides a suite of cloud computing services that runs on the same infrastructure that Google uses internally for its end-user products, such as Google Search, Gmail, and YouTube. Google Cloud supports data warehousing, data lakes, and data analytics services, as well as a range of IaaS, PaaS, and SaaS offerings.

📝 Enhancement Note: Capgemini's technology stack includes a mix of cloud data platforms, data warehousing tools, and big data technologies. By familiarizing yourself with these tools and technologies, you can demonstrate your expertise in data warehousing, data integration, and cloud data platforms.

👥 Team Culture & Values

Data Architecture Values:

Data-Driven Decision Making: Capgemini values data-driven decision-making and encourages data architects to design and implement data architectures that support business objectives and enable data-driven insights.
Data Quality & Governance: Capgemini emphasizes data quality and governance, ensuring that data is accurate, complete, and consistent across the organization. Data architects play a critical role in establishing and maintaining data governance frameworks and data quality initiatives.
Collaboration & Innovation: Capgemini fosters a culture of collaboration and innovation, encouraging data architects to work closely with stakeholders and explore new technologies and approaches to data management.
Customer Focus: Capgemini is committed to delivering exceptional customer value and expects data architects to design and implement data architectures that meet the needs of the business and support customer success.

Collaboration Style:

Cross-Functional Collaboration: Capgemini encourages data architects to work closely with teams from other disciplines, such as software development, business intelligence, and data analytics, to ensure that data architectures support business objectives and enable data-driven insights.
Agile Methodologies: Capgemini employs Agile methodologies, such as Scrum and Kanban, to manage projects and ensure efficient delivery of data products. Data architects work closely with development teams to ensure that data architectures are aligned with project requirements and support business objectives.
Continuous Improvement: Capgemini fosters a culture of continuous improvement, encouraging data architects to review and refine data architectures, processes, and tools to optimize performance and support business growth.

📝 Enhancement Note: Capgemini's data architecture values and collaboration style emphasize data-driven decision-making, data quality and governance, and customer focus. By embracing these values and working collaboratively with stakeholders, data architects can design and implement data architectures that support business objectives and enable data-driven insights.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Data Warehouse Design: Designing scalable and efficient data warehouses that support the needs of the business and enable data-driven insights. This challenge requires a deep understanding of data warehousing concepts, data modeling techniques, and data integration strategies.
Data Governance & Quality: Establishing and maintaining data governance frameworks and data quality initiatives that ensure data is accurate, complete, and consistent across the organization. This challenge requires a strong understanding of data governance principles, data quality tools, and data cataloging techniques.
Cloud Data Platforms: Designing, implementing, and managing data architectures in cloud data platforms, such as Snowflake, Databricks, and Spark. This challenge requires a deep understanding of cloud data platform-specific best practices, such as data partitioning, data compression, and data caching.
Data Integration & ETL: Developing and optimizing data integration and ETL processes that ensure data is accurate, complete, and consistent across the organization. This challenge requires a strong understanding of data integration tools, ETL techniques, and data transformation strategies.

Learning & Development Opportunities:

Technical Skills Development: Capgemini offers opportunities for technical specialization and certification in emerging technologies, such as cloud data platforms, big data technologies, and data governance tools. By developing your technical skills, you can enhance your expertise in data warehousing, data integration, and cloud data platforms.
Leadership Development: With experience and demonstrated success, data architects can progress to management roles, overseeing teams and driving strategic initiatives in data architecture and data management. By developing your leadership skills, you can advance your career and make a significant impact on the organization.
Cross-Functional Collaboration: Data architects can expand their skills and expertise by working on cross-functional projects with teams from other disciplines, such as software development, business intelligence, and data analytics. By collaborating with these teams, you can gain a deeper understanding of the business and develop your ability to design and implement data architectures that support business objectives.

📝 Enhancement Note: Capgemini's technical challenges and learning and development opportunities provide data architects with the chance to grow their skills, advance their careers, and make a significant impact on the organization. By embracing these challenges and pursuing continuous learning and development, data architects can excel in their roles and drive business success.

💡 Interview Preparation

Technical Questions:

Data Warehouse Design: What are the key considerations when designing a data warehouse for a large-scale e-commerce application? How would you approach optimizing data models and ETL processes for improved performance?
Data Governance & Quality: How would you establish and maintain a data governance framework for a data warehouse containing sensitive customer and financial data? What tools and techniques would you use to ensure data quality and consistency?
Cloud Data Platforms: How would you design and implement a data architecture in Snowflake, leveraging its unique features and capabilities? What are some best practices for optimizing data performance and cost-effectiveness in Snowflake?

Behavioral Questions:

Problem-Solving: Describe a time when you had to design and implement a complex data architecture to support a critical business initiative. How did you approach the challenge, and what was the outcome?
Collaboration: How have you worked effectively with cross-functional teams, such as software development, business intelligence, and data analytics, to deliver data products that align with business objectives? Can you provide an example of a successful collaboration and the role you played?
Adaptability: How have you adapted to changes in technology, data sources, or business requirements in a data architecture project? Describe a situation where you had to pivot or adjust your approach, and how you handled the change.

Portfolio Presentation Strategy:

Data Warehouse Design: Highlight your expertise in data warehousing solutions, ELT processes, and data integration techniques by showcasing your data warehouse designs, data models, and data flows. Explain the rationale behind your design choices and how they support business objectives.
Data Governance & Quality: Demonstrate your understanding of data governance, data quality, and data cataloging principles by providing examples of data governance frameworks, data quality initiatives, and data cataloging efforts. Explain how these initiatives ensure data accuracy, completeness, and consistency.
Cloud Data Platform Projects: Showcase your experience with cloud data platforms, such as Snowflake, Databricks, and Spark, by including projects that highlight your ability to design, implement, and manage data architectures in the cloud. Explain how these projects leverage cloud technologies to support business objectives.

📝 Enhancement Note: Capgemini's interview process focuses on assessing technical skills, problem-solving abilities, and cultural fit. By preparing thoroughly and showcasing your expertise in data warehousing, data integration, and cloud data platforms, you can demonstrate your qualifications for the Cloud Data Architect position.

📌 Application Steps

To apply for this Cloud Data Architect position at Capgemini:

Customize Your Resume: Tailor your resume to highlight your relevant experience, skills, and achievements in data warehousing, data integration, and cloud data platforms. Include specific examples of your work with Snowflake, Databricks, Spark, and other relevant tools and technologies.
Prepare Your Portfolio: Showcase your expertise in data warehousing, data integration, and cloud data platforms by including examples of your data warehouse designs, data models, and data flows. Highlight your ability to design, implement, and manage data architectures in the cloud, and demonstrate your understanding of data governance, data quality, and data cataloging principles.
Practice Technical Challenges: Familiarize yourself with Capgemini's technology stack and prepare for technical challenges related to data warehousing, data integration, and cloud data platforms. Practice problem-solving questions and brush up on your knowledge of data governance, data quality, and data cataloging principles.
Research Capgemini: Learn about Capgemini's company culture, values, and commitment to innovation, sustainability, and social responsibility. Understand how Capgemini's data architecture team supports business objectives and enables data-driven decision-making.
Prepare for Behavioral Questions: Reflect on your past experiences and prepare for behavioral questions that assess your problem-solving skills, collaboration, adaptability, and cultural fit. Provide specific examples of your work with cross-functional teams, your approach to challenges, and your ability to adapt to changes in technology, data sources, or business requirements.

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

Cloud Data Architect