Role Description We’re looking for a Staff Data Engineer to join a multi-disciplinary engineering team building modern, enterprise-grade data platforms. This role is ideal for an experienced engineer who can define data strategy, own platform decisions end-to-end, and contribute to technical leadership across the team. In this role, you will: - Design scalable data lakes, warehouses, and pipelines. - Define governance and quality standards. - Drive data platform modernization across real, in-flight work where performance, reliability, and security are critical. - Mentor more junior engineers. - Partner with leadership on data strategy. - Bring an AI-forward mindset. What You’ll Do Craft & Delivery - Define data architecture and platform strategy, leading design across pipelines, warehouses, and data lakes. - Build and optimize scalable data pipelines supporting batch and real-time processing. - Define and enforce data governance, quality standards, and compliance frameworks across the platform. - Build monitoring, logging, and alerting for data pipelines and services, and contribute to CI/CD workflows for data deployment and automation. - Drive data platform modernization, optimizing for performance, cost, and scalability. - Bring an AI-forward mindset to your daily work, using tools like Claude, Cursor, and other modern AI assistants to ship higher-quality work at pace. - Design and implement data contracts and event flows in collaboration with backend, platform, and engineering teams. - Lead the design and implementation of data pipelines for production AI/ML systems, including embeddings, vector stores, RAG data preparation, feature stores, and training/inference data flows. - Integrate data services with APIs, middleware, and third-party systems to support end-to-end data consumption. Collaboration & Communication - Partner with leadership on data strategy, translating technical depth into decisions others can act on. - Collaborate closely with engineering, analytics, AI, and product teams to align data platforms with broader goals. - Advocate for data quality, governance, and platform best practices across teams and engagements. Leadership & Influence - Establish data engineering standards that lift the quality and consistency of work across the team. - Mentor junior and mid-level engineers, helping them grow their craft, confidence, and impact. - Make high-stakes architectural decisions with clear ownership and consideration of long-term tradeoffs. Qualifications - 7+ years of professional data engineering experience, with experience leading complex data platform initiatives. - Strong system architecture background with expertise in distributed data systems. - Expert proficiency in Python, Scala, and SQL. - Deep expertise with cloud-native data platforms and enterprise data warehousing. - Strong expertise in data pipeline orchestration and processing. - Strong experience with streaming platforms and real-time data processing (e.g., Kafka, Kinesis, Pub/Sub). - Strong data modeling expertise and experience with data transformation. - Strong experience with data quality, governance, and compliance frameworks. - Strong experience with container orchestration and CI/CD for data systems. - Strong experience building data pipelines for production AI/ML systems, including embeddings, vector stores, RAG data preparation, feature stores, and training/inference data flows. - Demonstrated leadership and technical mentoring experience across a team or organization. - Strong stakeholder communication skills, with the ability to translate technical depth across audiences. - Demonstrable, day-to-day usage and expert knowledge of AI-forward coding tools such as Claude and Cursor. - Excellent problem-solving skills and the ability to navigate highly ambiguous technical and business challenges with sound judgment. - Experience with data mesh or data fabric concepts, lakehouse architectures, or governance framework implementation is a plus. Helpful Extras and Unique Skills - Experience with handling and modeling data in the healthcare industry is a plus. - AWS certifications, like Certified Data Engineer – Associate, strongly preferred. Benefits - A doer who sees something broken and fixes it. - A fast learner who embraces the changing AI landscape. - Direct in a way that improves the work. - Obsessed with craft and detail. - Built for ownership and accountability. - All in for clients' businesses. - Resourceful under constraints. - Glad to collaborate with dedicated team members.

Role Description - Arquitetar soluções escaláveis e seguras utilizando as melhores práticas da arquitetura Databricks; - Desenvolver e implementar estratégias eficazes de gerenciamento de projetos, utilizando ferramentas como Jira e Git para garantir transparência e eficiência; - Colaborar com equipes de produto e design para garantir a entrega de soluções alinhadas às expectativas dos clientes e aos objetivos do negócio; - Identificar e resolver problemas técnicos complexos, mantendo um ambiente de desenvolvimento ágil e colaborativo; - Liderar e orientar equipes multidisciplinares no desenvolvimento de produtos de dados, garantindo qualidade, escalabilidade e cumprimento de prazos. Qualifications - Sólido conhecimento e experiência prática em arquitetura Databricks; - Experiência comprovada em integração e implementação de soluções de engenharia de dados; - Vivência com Jira e Git para controle de versão e gerenciamento de projetos; - Forte compreensão das melhores práticas de segurança e governança no Databricks, incluindo Unity Catalog; - Conhecimento em bancos de dados relacionais e não relacionais, como PostgreSQL, DynamoDB e Redis. Requirements - Triagem de currículos; - Bate-papo com equipe de Gestão Humana; - Entrevista Técnica com lideranças do time; - Entrevista Final com cliente. Benefits - Vales Alimentação e Refeição (Swile); - Cobertura de até 100% em Plano de Saúde e Odontológico (Amil); - Seguro de Vida em grupo; - Trabalho remoto; - Convênio Saúde Mental - psicoterapia online e presencial; - Incentivo a certificações e cursos; - Convênio para cursos de pós-graduação e MBA (Esalq/USP); - Parceria com escolas de idiomas; - Parceria com academias e apps de bem-estar (Wellhub); - Palestras e rodas de conversa internas; - Bônus por indicação; - Happy hours; - Mimos em datas comemorativas. Company Description Acreditamos que os dados são o combustível para a transformação das empresas e das pessoas. Com isso em mente, ajudamos nossos clientes a criar soluções analíticas que os inspiram a realizar transformações organizacionais com impacto direto nos seus modelos de negócio. Na Five Acts, valorizamos a diversidade, a equidade e a inclusão. Acreditamos que a diversidade é uma força impulsionadora da inovação e do crescimento. Portanto, não fazemos distinção por questões de gênero, orientação sexual, religião, idade, etnia, ou qualquer outra. Nossas oportunidades são abertas para todas as pessoas!

Role Description The Data Engineer will have a strong background in data engineering and extensive experience with AWS Cloud services. As a Data Engineer, they are responsible for designing, building, and maintaining scalable data pipelines and infrastructure to support our data analytics and business intelligence initiatives. - Design, develop, and maintain robust data pipelines and ETL processes to ingest, transform, and store data from various sources. - Collaborate with data scientists, analysts, and other stakeholders to understand data requirements, design data models, and deliver solutions that meet business needs. - Automate data workflows and implement monitoring and logging to ensure the health and performance of the data infrastructure. - Conduct data profiling, cleansing, and validation to ensure high data quality standards. - Optimize data storage and retrieval performance, ensuring data quality and integrity. - Implement and manage data architecture on AWS, ensuring scalability, reliability, and security. - Stay up to date with the latest trends and best practices in data engineering and AWS cloud technologies. Qualifications - Bachelor’s or master’s degree in computer science, Information Technology, or a related field. - 3 or more years of related professional experience. - Experience in data engineering with a strong focus on AWS cloud services. - Proficiency in SQL and experience with relational databases (e.g., PostgreSQL, MySQL, Redshift). - Experience with AWS services such as S3, Lambda, Glue, EMR, Kinesis, and Redshift. - Strong programming skills in languages such as Python, Java, or Scala. - Knowledge of data modeling, ETL concepts, and data warehousing. - Familiarity with version control systems (e.g., Git) and CI/CD pipelines. - Excellent problem-solving skills and attention to detail. - Knowledge of machine learning frameworks and data science workflows. - Familiarity with data visualization tools (e.g., QuickSight, Qlik). - Familiarity with NoSQL databases (e.g., DynamoDB, MongoDB). - Strong collaboration skills with cross-functional teams to establish best design and user flows for applications. - Strong multitasking, problem solving, and organizational skills. - Proven ability to work independently and in a team environment. - Satisfactory background check required. Benefits - Flexible Approved Time Off - Tuition Reimbursement - 401k Retirement Plan - Work From Home Anywhere in the US - Maternal/Paternal Leave - Casual Dress Code & Work Environment

• Design and manage scalable data platforms powering real-time analytics, batch processing, and exploratory analysis, using AI-assisted development as the default workflow, not an afterthought. • Own the full data lifecycle: ingestion, ETL, storage, and serving, building and iterating on pipelines with AI pair-programming tools (Claude Code) to accelerate delivery. • Ingest data from diverse sources via both streaming (Kafka, Kinesis) and batch pipelines, unifying them into a consistent, queryable platform. • Architect medallion-layer data models (Bronze/Silver/Gold) in Databricks, ensuring business needs are met with clean, well-documented schemas. • Automate, test, and harden data workflows, writing AI-augmented tests, data quality checks, and CI/CD pipelines that catch issues before production. • Build and maintain AI-ready tooling: craft prompts, custom slash commands, and agent workflows that let the entire team scaffold pipelines, generate documentation, and validate data quality faster. • Build and improve Databricks Genie chatbots that allow non-technical users to query data using natural language. • Collaborate with product analytics and data science, applying engineering rigor to messy, unstructured data and transforming it into reliable, production-ready datasets. • Contribute to infrastructure-as-code (Terraform/Atmos) for provisioning and managing cloud data infrastructure.