Rox Partner

• Define, evolve and disseminate the Data and Artificial Intelligence Reference Architecture, establishing standards, frameworks and best practices across the group. • Diagnose the current Data and AI landscape and propose scalable, resilient architectures aligned with business strategy. • Lead the evolution of the corporate data platform based on Databricks, ensuring scalability, governance and high availability. • Design and evolve the corporate AI Agents platform, promoting the use of Generative AI and AI-First architectures to accelerate solution development. • Define standards for ingestion, transformation, integration, modeling, quality, security, governance and data provisioning. • Implement and promote Data Mesh, Data as a Product and Federated Governance practices, enabling domain autonomy with shared corporate standards. • Design and develop reusable components, pipelines, models, AI agents, frameworks and accelerators to optimize squad delivery. • Structure and evolve a corporate marketplace for sharing data products, models, agents, components and reusable assets. • Provide technical leadership for the architecture and implementation of strategic Data and Artificial Intelligence initiatives, ensuring compliance with corporate guidelines. • Serve as a technical reference for Data Engineering, Data Science, Analytics and AI teams, promoting best practices, architectural reviews and continuous improvement. • Collaborate with Engineering, Cloud, Infrastructure, Security, Governance, Product and Business teams to build a modern, secure platform prepared for organizational growth. • Keep up with trends, technologies and best practices related to Data Architecture, Generative AI, MLOps, DataOps and Platform Engineering, fostering continuous innovation.

• Join our Data team as a Mid-level Data Analyst • Work on Business Intelligence and Data Engineering projects • Develop, optimize, and troubleshoot complex SQL queries • Build tables and data pipelines using GCP and Power BI • Collaborate with other departments to gather requirements and develop analytical solutions

• Develop, validate, monitor, and evolve statistical, predictive, and Machine Learning models to support strategic business initiatives • Explore, analyze, and interpret large volumes of structured and unstructured data, turning them into actionable insights for decision-making • Conduct analytical studies and build models to understand customer behavior, identify patterns, and generate business opportunities • Design and implement scalable analytical solutions, ensuring result quality, efficiency, and reliability • Perform exploratory data analysis, hypothesis testing, and impact assessments to support strategic decisions • Identify opportunities to optimize processes using Data Science, Statistics, and Artificial Intelligence techniques • Work with data from various internal and external sources, ensuring integration, consistency, and data quality • Collaborate with Business, Data Engineering, and Technology teams to understand requirements and translate challenges into analytical solutions • Monitor the performance of models in production and propose continuous improvements to ensure accuracy and efficiency • Research, evaluate, and apply new methodologies, algorithms, and technologies related to Artificial Intelligence, Machine Learning, and Analytics to promote innovation and advance data solutions.

• Lead the definition of the enterprise Data and Artificial Intelligence architecture for the entire Supply Chain, ensuring scalability, governance and high availability of solutions. • Define architecture standards for ingestion, processing, storage and consumption of data coming from SAP, legacy systems, corporate applications, spreadsheets and other business sources. • Design Data Lakes, Data Warehouses, Data Products and event-driven architectures using Google Cloud Platform services. • Act as the main technical reference between business areas, Data Engineering, Analytics, Data Science and Technology, translating operational challenges into technological solutions. • Define architectures for Artificial Intelligence, Machine Learning and Generative AI applications focused on Supply Chain. • Develop strategies for demand forecasting, inventory optimization, loss identification, replenishment planning, logistics management and operational efficiency using predictive models and AI. • Design modern data pipelines using DataOps, MLOps and CI/CD best practices. • Ensure adherence to corporate policies for security, governance, data quality and observability. • Define integration strategies between Data, AI, SAP, APIs and other corporate systems. • Drive the continuous evolution of the data platform, proposing new technologies and architectures aligned with market best practices. • Support the construction of the technology roadmap for the area, identifying opportunities for innovation and value creation for the business. • Conduct technical assessments (As-Is and To-Be), proposing scalable architectures to support operational growth over the coming years. • Influence strategic decisions related to the evolution of the company’s data and Artificial Intelligence platform.

• Realizar o deploy, orquestração e sustentação de aplicações de IA em ambientes produtivos de alta disponibilidade; • Desenvolver e manter integrações complexas entre sistemas utilizando APIs REST/GraphQL, API Gateways e Load Balancers; • Construir pipelines e fluxos de processamento de dados para aplicações de IA utilizando Python em ambiente AWS; • Projetar, otimizar e sustentar arquiteturas baseadas em Agentic AI e comunicação Agent-to-Agent (A2A); • Implementar e dar suporte a integrações utilizando MCP (Model Context Protocol); • Atuar na evolução e manutenção de fluxos de OCR e extração de dados não estruturados utilizando LLMs; • Criar, gerenciar e evoluir dashboards avançados de monitoramento e observabilidade, garantindo a saúde dos modelos, performance de inferência, disponibilidade das aplicações e controle de custos operacionais utilizando Grafana, Rancher e ferramentas correlatas; • Monitorar e otimizar aplicações de IA em produção, garantindo escalabilidade, estabilidade e eficiência operacional; • Identificar e resolver problemas de performance, gargalos e falhas em pipelines de IA; • Trabalhar em conjunto com times multidisciplinares de engenharia, dados, arquitetura e produto

• Develop, evolve, and maintain scalable data pipelines in a Google Cloud Platform (GCP) environment • Design and implement data ingestion, processing, and storage solutions using services such as BigQuery, Dataflow, Pub/Sub, Cloud Storage, and Cloud Functions • Develop and optimize ETL/ELT processes, ensuring performance, reliability, and data quality • Create data models to support analytics, Business Intelligence, and Data Science initiatives • Monitor, diagnose, and optimize the performance of pipelines and data processing workloads • Implement best practices for governance, security, observability, and data quality • Collaborate with Software Engineering, Analytics, Data Science, and business teams to understand requirements and develop data-driven solutions • Contribute to the evolution of the data architecture, proposing continuous improvements in scalability, cost, and performance • Follow development best practices, automation, and continuous integration/continuous deployment (CI/CD).

• Serve as the interface between business, Data Engineering, Analytics, Data Science and Architecture teams, ensuring alignment between needs and deliveries; • Gather, document and prioritize functional and technical requirements with stakeholders; • Manage and prioritize the Product Backlog, defining epics, features and user stories; • Oversee the full development lifecycle of data products, from conception through deployment and continuous evolution; • Support the definition of data architectures on Google Cloud Platform (GCP), ensuring scalability, governance and solution quality; • Contribute to defining business indicators (KPIs) and success metrics for products; • Ensure solutions adhere to data governance, quality, and security practices; • Collaborate with Data Engineering and Data Science teams to implement analytical models and AI-based solutions; • Facilitate agile ceremonies, refinements, prioritizations and alignments with multidisciplinary teams; • Identify opportunities for innovation and continuous improvement in data and Supply Chain processes.

• Develop, maintain and optimize data transformation pipelines using DBT. • Build and manage complex data workflows using Apache Airflow. • Design and implement dimensional and relational models to support analyses and strategic KPIs. • Ensure data quality, reliability and traceability through testing, monitoring and engineering best practices. • Work on modeling, organizing and provisioning datasets for analytical consumption. • Develop and optimize high-performance SQL queries over large data volumes. • Work with BigQuery, driving operational efficiency, cost optimization and performance. • Develop and maintain dashboards and semantic layers using the Looker platform. • Promote documentation, governance and data cataloging best practices. • Support business areas in defining metrics, KPIs and analytical requirements. • Participate in architectural discussions and contribute to the evolution of the organization’s data platform. • Automate processes and routines using Python when required. • Serve as a technical reference for the team, sharing knowledge and best practices.

• Continuously monitor batch, streaming and on-demand data pipelines to ensure availability and data quality. • Identify, analyze and resolve operational incidents and critical failures in data environments. • Perform advanced troubleshooting by analyzing logs, metrics and alerts on GCP platforms. • Monitor and support data workflows, including DAGs, reprocessing, dependencies, and load validations. • Track execution of processes in Dataproc, Spark, BigQuery and other components of the data architecture. • Collaborate with Data Engineering, Architecture, Governance and Business teams to resolve issues and drive continuous improvements. • Contribute to the stability, reliability and evolution of data environments. • Prepare and maintain incident documentation, root cause analyses (RCA), operational procedures and action plans. • Participate in automation, observability and operational process optimization initiatives.

• Work closely with business areas to understand needs and translate requirements into scalable data solutions aligned with the organization’s objectives. • Conduct requirements gathering and map business processes and data flows between systems. • Define and validate business rules, metrics, KPIs and corporate indicators. • Design and evolve data models for Data Warehouse, Data Lake and Lakehouse environments. • Define dimensional modeling strategies, including facts, dimensions, granularity and relationships. • Develop data architectures using Azure Data Factory, Databricks, Data Lake and Power BI. • Ensure data quality, consistency and traceability across data pipelines. • Define standards for data ingestion, transformation, quality and availability across Bronze, Silver and Gold layers. • Serve as a technical reference for Data Engineering, BI and Analytics teams. • Support solution implementation, ensuring adherence to architectural and business definitions. • Produce documentation for data models, business rules and corporate indicators.