• Design and maintain ingestion pipelines (Python-based ETL/ELT) • Design structured transformation workflows using dbt, SQLMesh, or equivalent • Convert unstructured transcripts and documents into normalized database records • Maintain PostgreSQL architecture (structured tables, JSONB, indexing strategy) • Develop attribute extraction frameworks for technical, commercial, and risk signals • Ensure data quality, consistency, and lineage from raw interaction to structured output • Collaborate with AI/ML engineers to ensure clean model inputs

• Own and evolve Koala Health’s end-to-end data infrastructure, including ingestion, transformation, modeling, and delivery. • Design and maintain reliable data pipelines from production systems (e.g., application databases, third-party tools, vendors). • Build and manage data models that support analytics, reporting, and operational use cases. • Establish and enforce best practices for data quality, testing, monitoring, and documentation. • Partner with stakeholders across product, operations, finance, and marketing to understand data needs and translate them into scalable solutions. • Improve the reliability, performance, and cost-efficiency of the data stack as the business grows. • Own incident response and debugging for data issues, proactively identifying and resolving root causes. • Create and maintain clear documentation so data assets are understandable and usable across the company. • Evaluate and implement tooling improvements where it meaningfully improves developer velocity or data quality. • Act as a thought partner to leadership on how data can better support decision-making and operational efficiency.

This description is a summary of our understanding of the job description. Click on 'Apply' button to find out more. Role Description A ROIT é uma empresa disruptiva e inovadora. Desenvolvemos e disponibilizamos ao mercado soluções de tecnologia disruptivas, baseadas no uso humano como exceção. Oferecemos uma transformação inteligente para empresas que desejam evoluir e alcançar resultados surpreendentes com Robotização, Inteligência Artificial e Analytics. Venha fazer parte dessa transformação! Responsibilities - Implementar sistemas e rotinas de monitoramento (dados, aplicações, queries etc); - Evoluir modelos de dados, arquitetura e construção de pipeline de dados para atender novos requisitos de engenharia e negócios; - Implementar rotinas de migração, tratamento e armazenamento de dados (ETL); - Desenvolver integrações entre diferentes fontes de dados (RDS, APIs externas e etc) a fim de centralizar em um datalake; - Implementar e conduzir testes de carga; - Monitorar pipeline de dados em execução. Qualifications - Experiência com Python; - Experiência com GCP; - Experiência com integração continua e deploy em cloud; - Experiência com Datalake, Data Warehouse e Data Marts; - Conhecimento em banco de dados SQL e NoSQL; - Conhecimento em recurso das plataformas cloud; - Conhecimento em arquitetura orientada a eventos. Requirements - Será um diferencial ter experiência em: - Vivência em projetos de streaming; - Experiência com Airflow; - Processamento distribuído de dados (Spark ou similares).

• Deliver Flex-critical data needs: build and maintain reliable pipelines and datasets that enable Flex models (e.g., demand/availability signals, aggregations, monitoring). • Evolve the data platform: assess what we have today and drive pragmatic improvements in architecture, tooling, and operating practices. • Own data quality and trust: implement testing, lineage/definitions, and guardrails (e.g., dbt tests, anomaly detection, freshness checks) so stakeholders can trust outputs. • Enable self-serve analytics: produce well modeled datasets and documentation that make it easy for others to answer questions without bespoke work. • Partner on data science work: collaborate on data readiness for modelling, feature pipelines, evaluation workflows, and productionization concerns (even if you’re not the primary model builder). • Make high-leverage tech choices: propose and justify changes (or non-changes) to tools and processes, prioritizing impact and delivery over long platform rewrites.