• Apoiar ativamente na definição de requisitos para a área de infraestrutura de TI/Dados, contribuindo com análises técnicas sobre soluções e alternativas propostas, avaliando impactos em performance, custo, escalabilidade e aderência à arquitetura existente; • Atuar com visão crítica moderada, sugerindo melhorias e identificando riscos técnicos, alinhado às diretrizes estabelecidas pelo time sênior e arquitetura corporativa; • Participar do desenho e implementação de arquiteturas de dados de média complexidade, seguindo padrões e diretrizes definidos pela arquitetura corporativa; • Atuar de forma colaborativa com equipes técnicas e não técnicas, traduzindo conceitos complexos em linguagem acessível para facilitar alinhamentos e decisões; • Contribuir ativamente para o sucesso dos projetos ao antecipar necessidades, apoiar outras áreas e participar da resolução de divergências técnicas com maturidade e foco em resultados; • Colaborar ativamente na definição e extração de dados provenientes de diferentes fontes para ingestão nas plataformas de Big Data, garantindo aderência aos padrões estabelecidos; • Apoiar a modelagem e organização das estruturas de dados para que estejam apropriadas, acessíveis e disponíveis para consumo, assegurando o alinhamento entre os dados operacionais e os modelos analíticos requeridos; • Contribuir ativamente nos projetos relacionados à integração de dados e sistemas, implementando soluções de média complexidade e garantindo aderência aos padrões arquiteturais definidos; • Apoiar o desenho técnico das soluções, participar da evolução da arquitetura existente e atua na manutenção e melhoria contínua dos sistemas sob sua responsabilidade.

• Responsible for integrating, analyzing raw data, developing, and maintaining datasets and improving data quality and efficiency. • Develops and maintains scalable data pipelines, integration tools and builds out new API integrations to support continuing increases in data usage, volume, and complexity. • Building analytical tools to utilize the data pipeline, providing actionable insight into key business performance metrics including operational efficiency and customer acquisition. • Designs data integrations, data quality framework and integrate it with monitoring services. • Strong hands on capabilities across integration toolsets including ADF, Replication, CDC etc. • Integrate data from different SOR’s, combine raw information and create resulting models ready for consumption. • Interpret trends and patterns. • Performs data analysis required to troubleshoot data related issues and assist in the resolution of data issues. • Identifying, designing, and implementing internal process improvements including re-designing infrastructure for greater scalability, DB, SQL, and data pipelines performance tuning, optimizing data delivery, and automating manual processes. • Build solution prototypes as needed and write algorithms against data. • Implements processes and systems to monitor data quality, ensuring production data is always accurate and available for key stakeholders and business processes that depend on it. • Collaborates with analytics and business teams to improve data models that feed business intelligence tools, increasing data accessibility, and fostering data-driven decision making across the organization. • Works closely with a team of frontend and backend engineers, product managers, and analysts.

• Owner of the core data pipeline, responsible for scaling up data processing flow to meet the rapid data growth at Lyft • Evolve data model and data schema based on business and engineering needs • Implement systems tracking data quality and consistency • Develop tools supporting self-service data pipeline management (ETL) • SQL and MapReduce job tuning to improve data processing performance • Write well-crafted, well-tested, readable, maintainable code • Participate in code reviews to ensure code quality and distribute knowledge • Collaborate cross-functionally with product, engineering, data science, and marketing teams to understand business problems and align on prioritization and solutions

• Lead and manage data engineering projects from conception to deployment, ensuring alignment with stakeholder needs. • Design, build, and maintain scalable data pipelines to support data ingestion, transformation, and storage. • Collaborate with data scientists and analysts to ensure that data needs are met and provide technical guidance on best practices. • Implement and maintain data quality and integrity measures. • Utilize cloud-based data platforms and technologies, particularly Azure, to enhance data accessibility and usability. • Mentor and guide junior team members in data engineering concepts and practices. • Identify opportunities for process improvements and implement solutions that enhance efficiency, scalability, and performance.