[Job - 30217] Senior Data Architect, Brazil

๐Ÿข CI&T ยท all CI&T jobs
๐Ÿ“ Brazil
๐Ÿ“… Posted 2026-07-06 ยท via Himalayas
๐Ÿท Senior-Data-Architect,Data-Architecture,Cloud-Architecture,Data-Engineering,AWS-Data,Data-Architect-Senior-Consultant,Senior-BI-Data-Architect,Lead-Data-Architect,Senior-Data-Science-Architect,Data-Architecture-Lead,Senior-Database-Architect
Apply on original site โ†—

At CI&T, we help large enterprises transform the potential of AI into real business impact with AI Deployment, AI-native execution, and tech-integrated business solutions.

With 30 years of experience in technological transformation, we accelerate innovation with expertise in Agentic SDLC, Application modernization, Data & AI, Martech and Business strategy.

We are 8,000 CI&Ters across more than 25 countries, collaborating to build solutions with real impact. AI is already part of how we work, evolve, and innovate every day.

At CI&T, we are expanding our data and AI capabilities to deliver transformative solutions for our global clients. We are seeking a Senior Data Architect who will serve as a trusted technical advisor, bridging business strategy and cutting-edge cloud architecture. This role is critical in enabling our clients to modernize their data infrastructure, unlock AI-driven insights, and build scalable, future-ready data platforms.

The Senior Data Architect will lead the design and implementation of cloud-native data architectures, with a strong emphasis on AWS-based solutions and AI/ML readiness. This position requires a blend of deep technical expertise, strategic thinking, and exceptional client relationship management. You will work at the intersection of legacy system transformation and modern data platform design, guiding cross-functional teams through complex architectural decisions while keeping business objectives at the forefront. This engagement is part of a broader AI-first hyper-personalization initiative, requiring an architect who treats AI and ML workloads as first-class use cases from day one.

Responsibilities:

Client Advisory & Strategic Alignment

-
Drive architectural strategy discussions with client leadership to ensure data platform initiatives align with business objectives and deliver measurable value

-
Act as the senior technical voice in client engagements, translating complex architectural decisions into business outcomes for both technical and executive audiences

-
Manage client relationships from an architectural perspective, building trust through technical excellence and strategic insight

Technical Leadership & Team Enablement

-
Lead cross-functional teams of data engineers, data scientists, and analytics specialists in designing and deploying scalable, cloud-native data platforms

-
Support teams in planning and execution, providing architectural guidance and removing technical blockers throughout the delivery lifecycle

-
Mentor team members on cloud architecture best practices, data modeling principles, and emerging technologies

Cloud Architecture & Modernization

-
Design and implement robust AWS-based data lake architectures using Medallion (Bronze/Silver/Gold) patterns, managing trade-offs in storage strategies, partitioning schemes, and schema evolution

-
Lead the transition of legacy data structures and systems to modern cloud platforms, developing migration strategies that minimize risk and maximize business continuity

-
Architect solutions that balance performance, cost, scalability, and maintainability across complex data ecosystems

Business-to-Data Translation

-
Partner with Data & Analytics Managers and business stakeholders to translate business requirements into feasible, scalable architectural decisions

-
Define data models, integration patterns, and platform capabilities that directly support business use cases and analytics needs

-
Navigate complex, siloed data landscapes to design practical solutions that deliver value incrementally

AI/ML-Ready Platform Design

-
Design data platforms that natively support AI and ML workloads, including feature engineering pipelines, feature stores, model training data preparation, and inference serving infrastructure

-
Architect MLOps capabilities as a core platform component, not an afterthought, ensuring seamless integration of machine learning lifecycle management

-
Implement solutions lev

โ† All remote jobs