• Define and lead end-to-end data architecture for complex ecosystems, balancing technical depth with business outcomes. • Translate business strategy into scalable technology solutions through discovery workshops, assessments, and roadmaps. • Act as technical sponsor for CI&T’s most strategic accounts, working closely with C-level clients and non-technical stakeholders. • Lead architectural design, technology selection, and proof-of-concepts for critical platforms and innovation programs. • Govern technical quality across delivery squads, driving adherence to security, performance, scalability, and privacy standards. • Support pre-sales, proposal development, RFPs, and technical visioning with account teams and clients. • Coach and mentor senior architects and technical leads, shaping technical career development paths.

• Design, build, and maintain scalable, high-quality data pipelines for structured and unstructured data. • Implement robust data ingestion, transformation, and storage using cloud-based technologies. • Collaborate with stakeholders to understand business goals and translate them into data engineering solutions. • Monitor, troubleshoot, and optimize data pipelines for reliability and performance. • Support data validation, testing, and documentation processes. • Contribute to the design and deployment of modern data architectures (e.g., data lakes, lakehouses, data warehouses). • Apply Infrastructure-as-Code (IaC) practices for provisioning and managing cloud resources. • Integrate emerging tools and frameworks to modernize existing data environments. • Ensure security, governance, and compliance in all stages of data handling. • Work in agile teams, contributing to continuous improvement and mentoring junior team members.

• Design, build, and support large-scale backend data pipelines on cloud data platforms (GCP/BigQuery) • Lead development and evolution of semantic layers, including universal / reusable semantic models that support multiple business domains and analytics tools • Design and maintain Analytical Data Store (ADS) data structures optimized for analytics, reporting, and advanced modeling. • Partner with multiple EDW product teams to ensure consistent data modeling, metric definitions, and access patterns • Drive platform reliability, scalability, and operational excellence across data products • Lead query performance tuning and cost optimization efforts (slot usage, partitioning, clustering, aggregates, workload management) • Support and modernize data orchestration frameworks • Implement and enforce data quality, validation, and monitoring frameworks • Perform deep root-cause analysis of data issues, pipeline failures, and performance regressions • Contribute to technical roadmaps, refactoring strategies, and platform modernization initiatives • Mentor and grow other engineers through design reviews, code reviews, and technical guidance • Create and maintain architecture diagrams, standards, and technical documentation

• Design and own scalable, secure data architectures and pipelines across our platforms (Azure Databricks, Microsoft Fabric, AKS, Power BI). • Collaborate closely with data engineers, analysts, MLOps Lead, AI and Data Architects, the Data & AI Governance team, and the Cloud Infrastructure team , as well as business stakeholders, to align architecture with business needs. • Define and maintain data models, governance frameworks, reference architectures, guidelines, best practices and operating principles for our data and analytics platforms. • Onboard and enable different types of users (data engineers, analysts, data scientists, power users) on the platform with clear standards, training material and recommended ways of working. • Mentor and provide architectural oversight to use-case teams, engineers and architects, ensuring solutions fit the overall data and platform strategy. • Drive innovation by identifying opportunities to leverage new tools and services in the Microsoft ecosystem and continuously improve our data platform and pipelines. • Ensure data quality, compliance, and observability across all data handling and data product lifecycles.