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FBS Data Information Architect

Capgemini

HybridHybrid

Original Job Summary

FBS Data Information Architect

Our client, one of the United States’ largest insurers, is seeking a Senior System Architect to design and implement complex system architectures. The role focuses on developing scalable data architectures to support personalized customer experiences, business intelligence, and advanced analytics.

Responsibilities

  • Define, create, and maintain architecture artifacts including data models and data dictionaries.
  • Develop scalable data architectures and optimize data pipelines that integrate various data sources.
  • Implement and maintain data warehousing solutions in Snowflake and support batch and real-time integration.
  • Collaborate with data engineers, analysts, and business stakeholders to translate requirements into data solutions.
  • Ensure compliance with data governance and security standards.

Required Skills & Technologies

  • Experience with Snowflake, Python, Azure, and AWS.
  • Strong foundation in Big Data, cloud platforms, and modern data architecture frameworks.
  • Expertise in data modeling, ETL/ELT, and data pipelines.
  • Hands-on experience in building data warehousing solutions.
  • Understanding of Customer Data Platforms, Master Data Management, and Customer 360 architectures.

Benefits & Perks

Competitive salary, performance-based bonuses, comprehensive benefits, career development, and flexible work arrangements in a dynamic and inclusive work culture.

Key skills/competency

  • Data Architecture
  • System Design
  • Snowflake
  • Python
  • Cloud Platforms
  • Big Data
  • ETL/ELT
  • Data Warehousing
  • Data Governance
  • Analytics

How to Get Hired at Capgemini

🎯 Tips for Getting Hired

  • Customize Resume: Tailor skills to data architecture challenges.
  • Highlight Experience: Emphasize Snowflake, Python, and cloud projects.
  • Research Capgemini: Study their technology partnerships and culture.
  • Prepare for Interviews: Practice problem-solving in data systems.

📝 Interview Preparation Advice

Technical Preparation

Review Snowflake implementation best practices.
Practice Python for data engineering tasks.
Brush up on AWS and Azure cloud services.
Study modern data architecture frameworks.

Behavioral Questions

Describe teamwork in previous data projects.
Explain a challenging data architecture experience.
Discuss handling tight deadlines and priorities.
Share a time when cross-team collaboration succeeded.