Cloud Data Architect at Capgemini

📍 Job Overview

Job Title: Cloud Data Architect
Company: Capgemini
Location: Montréal, Quebec, Canada
Job Type: On-site (Remote OK)
Category: Data Architecture
Date Posted: June 24, 2025
Experience Level: 7-12 years
Remote Status: Remote OK

🚀 Role Summary

Design and implement comprehensive data architecture strategies for Capgemini's Data on Snowflake Cloud Architecture.
Collaborate with cross-functional teams to deliver data products that align with business objectives and support data-driven decision-making.
Leverage expertise in data warehousing, cloud data platforms, and data integration techniques to create scalable and efficient data solutions.
Utilize advanced data modeling tools, stay updated with the latest trends in data technology, and maintain a strong understanding of business intelligence tools.

📝 Enhancement Note: This role requires a seasoned data architect with a strong background in cloud data platforms and a proven track record in data warehousing solutions. Familiarity with big data technologies and cloud services is essential, as is the ability to work effectively in collaborative, agile environments.

💻 Primary Responsibilities

Data Architecture Design: Develop and implement comprehensive data architecture strategies that support the needs of Capgemini's 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 strong focus on data governance, quality, and collaboration. The ideal candidate will have experience working with diverse teams and stakeholders to deliver data products that meet business needs and drive data-driven decision-making.

🎓 Skills & Qualifications

Education: Bachelor's degree in Computer Science, Information Technology, or a related field. A Master's degree would be an asset.

Experience: 7-12 years of experience in Data Warehousing, including at least 3+ years with cloud data platforms.

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 visualization tools (e.g., Tableau, Power BI).
Knowledge of data privacy regulations (e.g., GDPR, CCPA).
Familiarity with data pipeline orchestration tools (e.g., Apache Airflow, Luigi).

📝 Enhancement Note: While not explicitly stated, experience with data privacy regulations and data pipeline orchestration tools would be beneficial for this role, as they are increasingly important in data architecture and management.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

Include case studies demonstrating your experience in data architecture design, data modeling, and data governance.
Highlight projects where you have worked with cloud data platforms, big data technologies, and data integration techniques.
Showcase your ability to collaborate with cross-functional teams and deliver data products that meet business objectives.

Technical Documentation:

Provide detailed documentation of your data architecture designs, including data models, data flows, and data lineage.
Include any relevant code snippets or scripts used in your data ingestion frameworks.
Demonstrate your understanding of data quality by including data validation checks and data cleansing processes in your portfolio.

📝 Enhancement Note: As this role requires a strong focus on data governance and quality, be sure to include examples of how you have ensured data consistency, accuracy, and completeness in your portfolio. Additionally, highlight any data cataloging efforts you have led or contributed to.

💵 Compensation & Benefits

Salary Range: CAD 120,000 - CAD 160,000 per year (based on experience and market research for data architects in Montreal)

Benefits:

Competitive health, dental, and vision plans
Retirement savings plan with company matching
Employee stock purchase plan
Generous time-off policies, including vacation, sick leave, and holidays
Flexible work arrangements, including remote work options
Professional development opportunities, including training and certifications
Employee discounts on various products and services

Working Hours: Full-time position with standard business hours (Monday-Friday, 9:00 AM - 5:00 PM) and occasional flexibility for project deadlines and maintenance windows.

📝 Enhancement Note: The provided salary range is an estimate based on market research for data architects in Montreal. Capgemini's benefits package is competitive and includes health, retirement, and time-off benefits, as well as professional development opportunities.

🎯 Team & Company Context

🏢 Company Culture

Industry: Capgemini is a global leader in consulting, technology services, and digital transformation. They operate in various industries, including financial services, energy and utilities, manufacturing, life sciences, and public sector.

Company Size: Capgemini is a large organization with over 340,000 employees in more than 50 countries. This size allows for diverse career opportunities and exposure to various industries and technologies.

Founded: Capgemini was founded in 1967 and has since grown into a global leader in digital transformation and technology services.

Team Structure:

The data architecture team works closely with data engineers, data analysts, product owners, and other stakeholders to deliver data products that align with business objectives.
The team follows an agile methodology, with regular sprint planning, code reviews, and quality assurance practices.
Capgemini's data architecture team is part of the broader data and analytics practice, which includes data engineering, data science, and business intelligence capabilities.

Development Methodology:

Capgemini follows an Agile/Scrum methodology for software development, with regular sprint planning, daily stand-ups, and sprint retrospectives.
The company emphasizes code reviews, testing, and quality assurance to ensure high-quality data products.
Capgemini uses CI/CD pipelines and automated deployment strategies for efficient and reliable data delivery.

Company Website: Capgemini's website

📝 Enhancement Note: Capgemini's large size and global presence offer data architects significant opportunities for career growth and exposure to diverse industries and technologies. The company's focus on digital transformation and data-driven decision-making makes it an attractive choice for data professionals looking to make an impact in a dynamic environment.

📈 Career & Growth Analysis

Web Technology Career Level: This role is at the senior level, requiring a data architect with 7-12 years of experience in data warehousing, cloud data platforms, and data integration techniques. The ideal candidate will have a strong background in data architecture design, data modeling, and data governance, as well as experience working with cross-functional teams to deliver data products that meet business objectives.

Reporting Structure: The data architect will report directly to the data architecture manager and work closely with data engineers, data analysts, product owners, and other stakeholders to deliver data products that align with business objectives.

Technical Impact: The data architect will play a critical role in designing and implementing comprehensive data architecture strategies that support Capgemini's Data on Snowflake Cloud Architecture. Their work will enable data-driven decision-making, improve data quality, and enhance data governance.

Growth Opportunities:

Technical Growth: As a senior data architect, there is ample opportunity for technical growth and specialization in cloud data platforms, big data technologies, and emerging data trends.
Leadership Growth: With experience and strong performance, the data architect may have the opportunity to move into a leadership role, managing a team of data architects or taking on a more strategic role within the organization.
Career Transition: Capgemini's large size and diverse industry exposure provide opportunities for career transitions into related fields, such as data engineering, data science, or business intelligence.

📝 Enhancement Note: Capgemini's large size and diverse industry exposure provide significant opportunities for career growth and development. The company's focus on digital transformation and data-driven decision-making makes it an attractive choice for data professionals looking to advance their careers in a dynamic environment.

🌐 Work Environment

Office Type: Capgemini's Montreal office is a modern, collaborative workspace designed to facilitate cross-functional teamwork and innovation. The office features open-concept workspaces, meeting rooms, and breakout areas for team discussions and brainstorming sessions.

Office Location(s): Capgemini's Montreal office is located in the heart of the city's downtown core, with easy access to public transportation and amenities.

Workspace Context:

Collaborative Workspace: The open-concept workspace encourages collaboration and communication between team members, fostering a culture of knowledge-sharing and continuous learning.
Development Tools: Capgemini provides its data architects with access to the latest data modeling tools, big data technologies, and cloud services to facilitate efficient and effective data architecture design.
Team Interaction: The data architecture team works closely with data engineers, data analysts, product owners, and other stakeholders to ensure data architecture supports all aspects of the business. Regular team meetings and cross-functional collaboration sessions are held to align on data strategy and priorities.

Work Schedule: This role follows a standard full-time work schedule, with flexibility for project deadlines and maintenance windows as needed.

📝 Enhancement Note: Capgemini's modern, collaborative workspace and central location make it an attractive choice for data professionals looking for a dynamic and engaging work environment. The company's focus on cross-functional collaboration and knowledge-sharing fosters a culture of continuous learning and innovation.

📄 Application & Technical Interview Process

Interview Process:

Phone/Video Screen: A brief phone or video call to discuss your background, experience, and motivations for the role. Be prepared to discuss your data architecture experience and how it aligns with Capgemini's needs.
Technical Assessment: A hands-on technical assessment, focusing on your data architecture design, data modeling, and data governance skills. You may be asked to design a data model, create a data pipeline, or optimize an existing data architecture.
Behavioral Interview: A structured interview focusing on your problem-solving skills, communication, and collaboration abilities. Be prepared to discuss your experience working with cross-functional teams and delivering data products that meet business objectives.
Final Interview: A final interview with the data architecture manager or another senior leader to discuss your fit for the role and the team. This may include a discussion of your long-term career goals and how Capgemini can support your professional development.

Portfolio Review Tips:

Highlight your experience in data architecture design, data modeling, and data governance.
Include case studies demonstrating your ability to work with cloud data platforms, big data technologies, and data integration techniques.
Showcase your ability to collaborate with cross-functional teams and deliver data products that meet business objectives.
Include any relevant certifications or training in data architecture, cloud data platforms, or related technologies.

Technical Challenge Preparation:

Brush up on your data architecture design, data modeling, and data governance skills.
Familiarize yourself with Capgemini's data architecture and technology stack, including Snowflake, Databricks, Spark, and any other relevant tools.
Practice your problem-solving and communication skills, as you will be expected to explain your technical decisions and trade-offs to non-technical stakeholders.

ATS Keywords: (Organized by category)

Data Architecture: Data Warehousing, Cloud Data Platforms, Data Integration Techniques, Data Governance, Data Quality, Data Modeling, Data Lineage, Data Ingestion, Data Pipeline, Data Pipeline Orchestration, Data Cataloging, Data Governance Framework, Data Quality Framework
Cloud Services: AWS, Azure, Google Cloud, Snowflake, Databricks, Spark
Programming Languages: Python, SQL, Unix Shell Scripting
Data Modeling Tools: Erwin, Power Designer, Snowflake, Databricks
Big Data Technologies: Hadoop, Hive, Pig, Spark, Kafka, Flink, Storm
Data Visualization: Tableau, Power BI, QlikView, Looker
Data Privacy Regulations: GDPR, CCPA, HIPAA, PIPEDA
Collaboration Tools: JIRA, Confluence, Slack, Microsoft Teams
Methodologies: Agile, Scrum, Kanban, Waterfall
Soft Skills: Problem-solving, Communication, Collaboration, Leadership, Mentoring, Coaching

📝 Enhancement Note: Capgemini's interview process is designed to assess your technical skills, problem-solving abilities, and cultural fit. By preparing thoroughly and showcasing your experience in data architecture design, data modeling, and data governance, you will be well-positioned to succeed in the interview process.

🛠 Technology Stack & Web Infrastructure

Frontend Technologies: (Not applicable for this role)

Backend & Server Technologies:

Data Warehousing: Snowflake, Teradata, PostgreSQL, Sybase, DB2
Cloud Data Platforms: AWS, Azure, Google Cloud
Big Data Technologies: Databricks, Spark, Hadoop, Hive, Pig, Kafka, Flink, Storm
Data Modeling Tools: Erwin, Power Designer, Snowflake
Data Visualization: Tableau, Power BI, QlikView, Looker
Programming Languages: Python, SQL, Unix Shell Scripting

Development & DevOps Tools:

Version Control: Git, SVN
CI/CD Pipelines: Jenkins, GitLab CI/CD, CircleCI
Infrastructure as Code (IaC): Terraform, CloudFormation, Ansible
Containerization: Docker, Kubernetes
Monitoring: Prometheus, Grafana, New Relic, Datadog
Log Management: ELK Stack, Splunk, Logz.io
Cloud Services: AWS, Azure, Google Cloud, Snowflake

📝 Enhancement Note: Capgemini's technology stack includes a wide range of data warehousing, cloud data platforms, and big data technologies. Familiarity with these tools and a strong background in data architecture design, data modeling, and data governance are essential for success in this role.

👥 Team Culture & Values

Web Development Values:

Data-Driven Decision-Making: Capgemini emphasizes the importance of data-driven decision-making in all aspects of its business. The company values data architects who can design and implement data architectures that support data-driven decision-making and enhance data quality.
Collaboration and Knowledge-Sharing: Capgemini fosters a culture of collaboration and knowledge-sharing, with regular team meetings, cross-functional collaboration sessions, and mentoring opportunities.
Continuous Learning and Innovation: Capgemini encourages its employees to stay up-to-date with the latest trends in data technology and methodologies. The company values data architects who are curious, proactive, and committed to lifelong learning.
Customer-Centric Approach: Capgemini puts its customers at the center of everything it does. The company values data architects who can design and implement data architectures that meet business objectives and drive customer value.

Collaboration Style:

Cross-Functional Integration: Capgemini's data architecture team works closely with data engineers, data analysts, product owners, and other stakeholders to deliver data products that align with business objectives.
Code Review Culture: Capgemini emphasizes code reviews, testing, and quality assurance to ensure high-quality data products. The company values data architects who are willing to collaborate with peers and incorporate feedback into their work.
Knowledge Sharing and Mentoring: Capgemini encourages its employees to share their knowledge and expertise with their colleagues. The company values data architects who are willing to mentor and coach their peers and contribute to a culture of continuous learning and innovation.

📝 Enhancement Note: Capgemini's team culture emphasizes data-driven decision-making, collaboration, and continuous learning. The company values data architects who are curious, proactive, and committed to driving customer value through data-driven insights and innovative solutions.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Cloud Data Platforms: Staying up-to-date with the latest trends and best practices in cloud data platforms, such as AWS, Azure, and Google Cloud.
Big Data Technologies: Continuously learning and mastering new big data technologies and tools, such as Spark, Kafka, and Flink.
Data Governance: Ensuring data quality, consistency, and completeness in a large, distributed data environment.
Data Modeling: Designing and optimizing data models that support efficient data procurement, storage, processing, and analysis.
Data Integration: Developing and maintaining data ingestion frameworks that integrate data from diverse sources and ensure data consistency and accuracy.

Learning & Development Opportunities:

Technical Skills: Pursuing certifications and training in data architecture, cloud data platforms, big data technologies, and related tools to enhance your technical skillset.
Leadership Skills: Developing your leadership skills through mentoring, coaching, and project management opportunities.
Industry Knowledge: Staying up-to-date with the latest trends and best practices in data architecture, cloud data platforms, and big data technologies through industry events, conferences, and online resources.

📝 Enhancement Note: Capgemini's technical challenges and growth opportunities provide data architects with ample opportunities to develop their skills, advance their careers, and make a meaningful impact on the business. The company's focus on data-driven decision-making, collaboration, and continuous learning fosters a culture of growth and innovation.

💡 Interview Preparation

Technical Questions:

Data Architecture Design: Describe your experience designing and implementing comprehensive data architecture strategies for cloud data platforms. How do you approach data modeling, data governance, and data integration in a cloud-based data architecture?
Data Modeling: Walk us through your process for creating logical and physical data models that reflect business data consumption needs. How do you ensure data models support data mining, business intelligence, and analytics activities and AI tools?
Data Governance and Quality: Explain your approach to data governance and quality in a large, distributed data environment. How do you ensure data consistency, accuracy, and completeness, and how do you approach data cataloging and data discovery?
Collaboration: Describe your experience working with cross-functional teams to deliver data products that align with business objectives. How do you facilitate collaboration and knowledge-sharing between data architects, data engineers, data analysts, and other stakeholders?
Problem-Solving: Walk us through a challenging data architecture problem you've faced in the past and how you approached it. What was the outcome, and what did you learn from the experience?

Company & Culture Questions:

Data Architecture Strategy: How do you see Capgemini's data architecture strategy evolving in the next 3-5 years? What role do you see yourself playing in driving that strategy forward?
Data Governance and Quality: How do you approach data governance and quality in a large, distributed data environment like Capgemini's? What steps would you take to enhance data quality and ensure data consistency, accuracy, and completeness?
Collaboration and Knowledge-Sharing: How do you foster a culture of collaboration and knowledge-sharing within a data architecture team? What strategies do you use to encourage mentoring, coaching, and continuous learning?
Data-Driven Decision-Making: How do you ensure that Capgemini's data architecture supports data-driven decision-making and enhances data quality? What metrics do you use to measure the success of your data architecture designs?

Portfolio Presentation Strategy:

Data Architecture Case Studies: Highlight your experience in data architecture design, data modeling, and data governance by presenting case studies that demonstrate your ability to deliver data products that meet business objectives.
Data Pipeline and Ingestion: Showcase your experience in developing and maintaining data ingestion frameworks that integrate data from diverse sources and ensure data consistency and accuracy.
Data Governance and Quality: Demonstrate your approach to data governance and quality by including examples of how you have ensured data consistency, accuracy, and completeness in your portfolio.

📝 Enhancement Note: Capgemini's interview process is designed to assess your technical skills, problem-solving abilities, and cultural fit. By preparing thoroughly and showcasing your experience in data architecture design, data modeling, and data governance, you will be well-positioned to succeed in the interview process.

📌 Application Steps

To apply for this Cloud Data Architect position at Capgemini:

Tailor Your Resume: Highlight your experience in data architecture design, data modeling, and data governance. Include any relevant certifications or training in data architecture, cloud data platforms, or related technologies.
Prepare Your Portfolio: Include case studies demonstrating your experience in data architecture design, data modeling, and data governance. Showcase your ability to work with cloud data platforms, big data technologies, and data integration techniques.
Research Capgemini: Familiarize yourself with Capgemini's data architecture, technology stack, and company culture. Understand how Capgemini's data architecture strategy aligns with its business objectives and how you can contribute to its success.
Practice Technical Interview Questions: Brush up on your data architecture design, data modeling, and data governance skills. Practice problem-solving and communication skills, as you will be expected to explain your technical decisions and trade-offs to non-technical stakeholders.

⚠️ 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 Capgemini before making application decisions.

Cloud Data Architect