Role Description - Projetar, construir e manter a Plataforma Interna de Desenvolvimento (IDP) para as equipes de engenharia de dados. - Desenvolver e evoluir pipelines de CI/CD padronizados e reutilizáveis (ex: GitHub Actions, GitLab CI, Azure DevOps). - Gerenciar e escalar infraestrutura em nuvem (AWS, Azure ou GCP) utilizando Infraestrutura como Código (Terraform, Pulumi). - Criar e manter documentação técnica clara e guias de boas práticas para facilitar o onboarding de novos(as) desenvolvedores(as). - Desenhar e liderar a implementação da arquitetura de dados corporativa (Data Lakehouse, Data Warehouse, etc.) em ambiente de nuvem. - Definir e aplicar padrões de modelagem de dados (Dimensional, Data Vault) adequados para as diferentes camadas da arquitetura. - Estabelecer políticas de governança de dados, qualidade de dados, segurança, controle de acesso e conformidade (ex: LGPD). - Avaliar, selecionar e integrar novas ferramentas e tecnologias para a stack de dados moderna (Databricks, Snowflake, dbt, Airflow, Kafka). - Otimizar o desempenho e os custos de processamento de grandes volumes de dados (batch e streaming). Qualifications - Experiência sólida em Engenharia e Arquitetura de Dados, incluindo modelagem de dados, bancos relacionais e NoSQL, processamento distribuído (Spark) e ferramentas de orquestração e transformação de dados. - Experiência em arquitetura de nuvem pública (AWS, Azure ou GCP), Infraestrutura como Código (Terraform) e automação de deploys por meio de práticas e ferramentas de CI/CD. - Habilidade em programação/scripting para automação (Python, Go, ou Bash). - Familiaridade com conceitos de observabilidade. - Experiência em ambientes modernos de engenharia de dados, incluindo plataformas como Databricks, Airflow e ecossistemas analíticos em nuvem. - Fortes habilidades de comunicação para traduzir requisitos complexos de negócio em soluções técnicas. Benefits - 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

• Lead workspace architecture, Unity Catalog governance, and cluster policy design for client tenant organizations. • Perform tenant discovery and requirements gathering, source profiling, and security classification for new data intake requests. • Develop end-to-end technical designs for tenant onboarding, including Data Sharing Agreements and SLA documentation. • Maintain Requirements Traceability Matrix (RTM) and prepare Architecture Review Board (ARB) submissions. • Contribute to tenant onboarding automation, financial operations integration, and ROI reporting. • Deliver tenant training materials and facilitate weekly office hours for client program teams. • Support P1/P2 incident response within SLA for tenant-related platform issues. • Provide NIST 800-53 control evidence within 5 business days of the client audit requests.

Role Description The Senior AI Data Engineer will serve as both architect and builder of our data ecosystem. Every initiative will follow a complete engineering lifecycle: - Gathering stakeholder requirements - Designing the solution - Building and testing it - Shipping it to production This role will work across data lakes, analytics pipelines, and lightweight application development— the multi-disciplinary data equivalent of a full-stack developer. The Senior AI Data Engineer will work closely with the data science, finance, and clinical operations teams to design intelligent, automated data solutions that power care decisions, financial planning, and operational efficiency. AI augmentation is not optional — it is the standard working mode. Qualifications - 5+ years of hands-on data engineering experience, including building and operating production data pipelines - Expert-level Python skills for ETL, pipeline orchestration, and automation - Deep SQL proficiency — query optimization, data modeling, stored procedures - 2+ years’ experience working with AI first development workflows - 4+ years’ experience with AWS (S3, Glue, Lambda, Redshift) and/or Azure big data services - 1+ year of experience with Snowflake - 2+ years of experience with orchestration frameworks - 2+ years of Salesforce experience with Apex and configurations - Experience with Kimball dimensional modeling — building star schemas and conformed dimensions in production - Power BI (or equivalent BI tool) experience — data model design and report development - API integration experience — REST, GraphQL, event streaming (Kafka, Kinesis, or similar) - Application development literacy — comfortable building lightweight web tooling (Python/Flask, Node, or similar) Requirements - Engage stakeholders directly to gather, clarify, and document project requirements - Translate requirements into architected data solutions - Own testing end-to-end — unit tests, data quality checks, reconciliation, and integration tests before anything reaches production - Deploy solutions to production and monitor post-deployment health - Run parallel AI coding sessions across different facets of a pipeline simultaneously - Build and maintain context files for data projects - Design verification loops: automated data quality checks, dbt tests, CI hooks, and pipeline monitors - Build MCP (Model Context Protocol) or equivalent integrations - Design and build complex, reliable data pipelines ingesting from various sources - Implement and evolve data models using Kimball methodology - Optimize pipeline performance, manage data quality, and perform root-cause analysis on data anomalies - Develop and maintain orchestration workflows in Python, and AWS Glue - Continuously evolve the data schema as business and engineering requirements change - Build and support Power BI data models and reports - Work with data analysts and data scientists to build reusable, well-documented pipeline components - Deliver data products that drive clinical care decisions, financial planning, and operational performance improvements - Build lightweight internal data applications and tooling - Design for agentic workflows - Integrate with Salesforce Health Cloud and other platforms using APIs and event-driven patterns - Ensure data security and HIPAA compliance in all pipeline and application work - Document decisions, tradeoffs, and architecture clearly - Collaborate across IT, finance, clinical operations, and data science Benefits - Medical, dental, and vision insurance - 401(k) plan with company matching and rapid vesting - Paid holidays and wellness days - Life insurance and disability insurance options - Tuition reimbursements for professional development and continuing education - Referral bonuses Company Description Cortica is a rapidly growing healthcare company pioneering the most effective treatment methods for children with neurodevelopmental differences. Our mission is to design and deliver life-changing care – one child, one family, one community at a time. Ultimately, we envision a world that cultivates the full potential of every child. At Cortica, every team member is instrumental in helping us achieve our mission! Our culture and values guide how we work and treat one another. Cortica celebrates diversity and fosters an inclusive environment, seeking ideas and opinions from everyone on the team.

Role Description Nexaminds is looking for a Senior Databricks Engineer to lead the development, optimization, and scaling of our data solutions. The ideal candidate has hands-on expertise building end-to-end pipelines on Databricks and enjoys working in a fast-paced, highly collaborative environment. Job duties: - Design, build, and maintain scalable data pipelines using Databricks (SQL, Python, Spark). - Optimize ETL/ELT processes for performance, reliability, and cost efficiency. - Implement best practices for data quality, governance, and security. - Collaborate with data engineers, analysts, and stakeholders to define data requirements. - Troubleshoot and resolve issues across data workflows and distributed systems. - Support migration, modernization, or cloud data platform initiatives. Qualifications - 5+ years of experience in data engineering or similar roles. - Strong expertise with Databricks (Spark, Delta Lake, Workflows, SQL). - Proficiency in Python and SQL skills. - Solid understanding of cloud platforms (Azure or AWS). - Experience with data modeling, data warehousing, and distributed computing. - Shell Scripting. - Github, JFrog Artifactory. - Excellent communication and collaboration skills. - Strong written and verbal English proficiency. Benefits - Stock options 📈 - Remote work options 🏠 - Flexible working hours 🕜 - Benefits above the law. But it's not just about the work; it's about the people too. You'll be collaborating with some seriously awesome IT pros. You'll have access to mentorship and tons of opportunities to learn and level up.