[Job - 21458] Senior Data Architect, US

As a Senior Data Architect at CI&T, your main responsibilities will include defining the long-term vision for our data architecture, leading the evaluation of technologies, and collaborating with teams to ensure our data systems align with business strategies. You will also establish best practices for data governance, provide technical guidance, and mentor newer team members.

To be considered for the Senior Data Architect position at CI&T, you should have a bachelor's degree in Computer Science or a related field, along with at least 5 years of experience in data architecture or engineering. Proficiency with the Snowflake platform and familiarity with cloud environments are essential requirements as well.

At CI&T, we offer dedicated career advisors, ongoing mentorship opportunities, and a culture that values continuous learning, which are crucial for the career development of a Senior Data Architect. You’ll have numerous opportunities to expand your skills and grow into new roles within our organization.

Senior Data Architects at CI&T typically work with modern data architecture stacks in cloud environments. Experience with platforms like Snowflake is essential, along with knowledge of CI/CD pipelines for ETL processes and dimensional data modeling.

As a Senior Data Architect at CI&T, you can expect a competitive salary, generous paid vacation days, 100% paid health and dental benefits from day one, annual profit-sharing, retirement match, and paid parental leave. We also encourage a diverse and inclusive work environment that values your unique contributions.

When answering this question, focus on showcasing your leadership skills and technical expertise. Describe the project's objectives, the challenges you faced, and the innovative solutions you implemented. Highlight the technologies used, your management approach, and how you ensured successful collaboration with stakeholders.

It's important to discuss specific best practices like compliance with data regulations, implementing encryption, and defining access controls. Share experiences where you identified potential security risks and the proactive measures you took to secure the data architecture.

Elaborate on any projects where you utilized Snowflake, focusing on how you leveraged its features for performance and scalability. Mention any specific challenges you faced while using Snowflake and how you overcame them.

Discuss your approach to mentoring, which could include scheduled check-ins, providing constructive feedback, and offering learning resources. Highlight an example where your mentoring led to a significant improvement in a team member's performance.

Talk about the frameworks you've implemented for data governance, including creating policies and standards, and the importance of regular audits and compliance checks. Share your experience in fostering a culture of data stewardship within your teams.

Offer a brief overview of dimensional data modeling concepts and how you've applied them in past projects. Discuss specific techniques you've used to design fact and dimension tables, ensuring efficient data retrieval and analysis.

Mention strategies like attending conferences, participating in webinars, or engaging in online communities related to data architecture. Additionally, discuss any certification processes you are involved in and how they contribute to your professional growth.

Explain the significance of CI/CD in streamlining ETL processes. Discuss specific tools or platforms you've used for creating and managing pipelines and share examples of improvements in automation or efficiency you've achieved through these methodologies.

Discuss your understanding of scalable design principles. Provide examples of how you have designed systems anticipating future growth, including considerations for performance, data volume, and flexibility for adaptation.

Use this opportunity to illustrate your problem-solving skills. Discuss your approach to identifying mismatches in data quality, coordinating with different data teams, and ensuring a unified data architecture across the organization.