• Handle support tickets and operational issues reported by internal teams and external partners; investigate root causes and coordinate resolution with senior engineers • Perform KTLO (Keep The Lights On) tasks including monitoring pipeline health, responding to alerts, validating data quality, and investigating data anomalies • Conduct data source discovery and profiling work — examining raw data sources, documenting data structure, identifying quality issues, and recommending integration approaches • Assist with data validation and testing — writing SQL queries to validate data transformations, identifying gaps and inconsistencies, and flagging issues for review • Support data quality initiatives by running diagnostics, documenting data quality findings, and escalating issues with clear context for senior engineers • Assist in establishing and monitoring data quality metrics — working with senior engineers to define quality KPIs and track pipeline health • Help maintain and improve documentation for existing data systems, pipelines, and data sources — documenting schemas, transformation logic, and known issues • Assist senior engineers with debugging data pipeline issues — tracing data through transformations, validating intermediate outputs, and comparing expected vs. actual results • Conduct quality assurance activities — reviewing data outputs, testing transformations, and validating correctness before data reaches downstream consumers • Perform exploratory data analysis to understand data patterns, support analytics requests, and help answer business questions about data availability and quality • Learn and apply data engineering best practices including version control (Git), code review processes, and testing frameworks under guidance from senior engineers • Support infrastructure and operational tasks as assigned — assisting with deployments, maintaining environments, and supporting on-call activities • Participate in knowledge-sharing and mentorship; ask questions, document learnings, and contribute to team documentation and runbooks

• Desenvolver, evoluir e sustentar pipelines de dados escaláveis para ingestão, transformação e disponibilização de informações em ambiente Databricks, utilizando Python, SQL, Spark e DBT; • Projetar, implementar e optimizar processos de ETL/ELT, garantindo alta performance, confiabilidade, governança e qualidade dos dados ao longo do ciclo de vida das soluções; • Construir, manter e monitorar workflows e DAGs no Apache Airflow, assegurando a correta orquestração, automação e observabilidade das cargas de dados; • Atuar na modelagem e implementação das camadas Bronze, Silver e Gold seguindo a arquitetura Lakehouse e as melhores práticas de Data Engineering; • Integrar dados provenientes de múltiplas fontes, como APIs, bancos de dados relacionais e não relacionais, sistemas corporativos e serviços em nuvem; • Apoiar a definição de padrões técnicos, boas práticas de desenvolvimento, versionamento, testes e documentação de pipelines de dados; • Trabalhar em parceria com equipes de Analytics, BI, Data Science e áreas de negócio para entender requisitos e transformar necessidades em soluções de dados escaláveis; • Investigar e solucionar incidentes, problemas de performance e falhas em processos de dados, garantindo a estabilidade e disponibilidade da plataforma.

• Evolve our architecture into Next Generation Data Platform • Turn Business Data into AI-Ready Assets • Drive AI-First Data Engineering • Improve Business Intelligence & Data Accessibility • Lead Through Technical Excellence

• Build and maintain feature pipelines, training datasets, and forecast workflows for revenue, demand, delivery timing, customer behavior, inventory risk, process performance, and operational planning use cases. • Operationalize forecasting and machine learning models through repeatable training, evaluation, deployment, inference, and monitoring patterns in Databricks. • Deploy and support batch, near-real-time, and API-based inference outputs for dashboards, Databricks Apps, workflow automation, business alerts, and decision-support tools. • Implement model performance tracking, drift monitoring, validation checks, error handling, and traceability from source data through feature logic to prediction output. • Partner with Data Engineering and BI teams to ensure forecast outputs, KPIs, business logic, and AI-enabled metrics align to governed semantic structures and reporting standards. • Create reusable notebooks, libraries, feature engineering patterns, evaluation templates, and deployment frameworks that accelerate enterprise AI adoption while remaining supportable. • Support AI/BI and agent-based consumption by preparing structured, governed, business-readable outputs that can be used by reporting tools, applications, and AI assistants. • Translate forecasting and AI outputs into measurable operational or financial impact, including revenue opportunity, margin improvement, demand planning, service performance, inventory optimization, and process automation.