Role Description Na Vertigo, somos movidos por pessoas inquietas, que buscam resultado e têm coragem para atuar onde tecnologia e negócio se encontram. Somos uma consultoria de tecnologia com mais de 20 anos de inteligência digital, atuando em parceria com nossos clientes para transformar gaps tecnológicos em vantagens competitivas para gerar impacto real, acelerando negócios e melhorando vidas. Procuramos por um(a) Engenheiro de Dados Júnior que queira se desenvolver com a Vertigo! #GoVertigo 🚀 Responsabilidades e atribuições: - Apoiar o desenvolvimento e a manutenção de pipelines de dados (ETL/ELT) sob a supervisão de engenheiros mais experientes; - Auxiliar na estruturação e preparação de dados em ambientes analíticos (Data Lake e Lakehouse); - Ajudar na criação e manutenção de bases de dados, tabelas e views, familiarizando-se com modelos de dados (star schema, snowflake); - Contribuir nas rotinas de ingestão, transformação e limpeza de dados; - Colaborar com os demais membros da equipe de dados e áreas de negócio para facilitar o acesso e a disponibilidade das informações; - Seguir as diretrizes e boas práticas de segurança, privacidade e compliance (incluindo LGPD) definidas pela área; - Apoiar a documentação de processos, pipelines e dicionários de dados; - Utilizar versionamento de código (Git) em suas entregas diárias; - Participar ativamente das cerimônias ágeis (Scrum), colaborando nas entregas da sprint com dedicação e no prazo. Qualifications - Superior completo ou cursando o último ano em Tecnologia da Informação, Ciência da Computação, Engenharia, Estatística ou áreas correlatas; - Bons conhecimentos em SQL para consultas, manipulação e transformação de dados; - Conhecimento em programação aplicada a dados (Python é essencial; Scala ou R são diferenciais); - Familiaridade com conceitos de modelagem de dados; - Noções teóricas ou vivência acadêmica/estágio na construção de pipelines de dados; - Noções sobre plataformas de dados em nuvem e ambientes de processamento distribuído; - Conhecimento básico em controle de versão (Git). Requirements - Conhecimento básico ou cursos extracurriculares em plataformas como Databricks, MS Fabric ou Azure; - Familiaridade com ferramentas de orquestração (como Azure Data Factory ou Airflow); - Noções de arquitetura de dados moderna (Data Lakehouse, Delta Lake); - Conhecimento básico em ferramentas de visualização (como Power BI) para validação de dados; - Noções de automação de deploy (CI/CD). Benefits - Você quer fazer parte do nosso time? Se inscreva, e embarque nessa jornada com a gente!

Responsibilities: - Translate business needs and architectural guidance into detailed designs, data contracts, and implementation plans that break down large initiatives into actionable engineering tasks with reliable estimates - Create detailed pipeline designs covering schemas, transformations, partitioning, DLT configurations, orchestration, error handling, and observability that align with the platform architecture through close collaboration with the Data Architect - Lead implementation and guide junior engineers on design, coding standards, and best practices - Develop metadata-driven and configuration-driven pipeline patterns that reduce custom code and improve consistency - Make technical decisions that ensure reliability, performance, maintainability, and scalability. Ensure production readiness with monitoring, lineage, alerting, observability, CI/CD and documentation - Define and enforce engineering design patterns, coding standards, testing practices, and operational best practices - Evaluate and incorporate new technologies and Databricks capabilities that improve reliability, performance, or developer productivity - Validate new technologies with the Data Architect and operationalize them through documentation, examples, and enablement - Implement automated data quality checks, rule enforcement, and exception handling - Production support of both an existing and new platform including optimization of jobs, incident tracking and other analysis required for production - Lead resolution of complex production issues and deliver durable root cause fixes - Maintain SLAs for reliability, recovery, idempotency, performance, and cost efficiency - Mentor Level 2–3 engineers through pairing, design guidance, code reviews, and technical coaching Basic Skills: • Bachelor’s degree preferred; equivalent experience accepted • 10+ years in data engineering (12+ without a degree) • 4+ years building production-grade batch/streaming pipelines using PySpark, Spark Structured Streaming, Python, and SQL • Proven experience with data governance, schema evolution, data lineage, and secure access patterns • Proven 2 years’ experience with maintaining and sustaining data pipelines Preferred Skills: • 3+ years hands-on with Databricks (Delta Lake, DLT, Unity Catalog, workflow jobs) within the last 6 years • Experience building metadata-driven or configuration-driven pipelines • Experience with data quality frameworks (DQX, Great Expectations, or equivalent) • Experience with observability, metrics and query performance analysis • Strong Spark optimization Work Conditions: • Team collaborative hours between 8am to 4pm EST • Corporate office/lab environment • Ability to travel 10% of the time

• Design and develop high‑performance data converters for multi‑sensor autonomous‑driving data (camera, lidar, radar), ensuring accurate time alignment and robust handling of raw sensor logs. • Design, build, and optimize large‑scale ingestion and transformation pipelines (ETL/ELT) capable of processing petabyte‑scale autonomous‑driving sensor data, and automate them for reliable, production‑grade deployment. • Work with data formats such as ROS bags, MCAP, and custom binary encodings; establish standards for schema evolution and metadata integrity. • Implement automated data validation, quality checks, and lineage tracking to ensure reliability of production datasets. • Collaborate closely with ML, annotation, simulation, and perception teams to ensure cross‑team ownership of data products and deliver datasets that are consistent, semantically correct, and ready for downstream consumption. • Proactively assess current capabilities to identify areas for improvement proposing solutions that align with core strategy and operation.

• Design, develop, and maintain scalable real-time data pipelines to support analytics and operational use cases. • Own and operate cloud-based infrastructure, following a 'you build it, you run it' model to ensure reliability, performance, and scalability. • Develop and manage data products, collaborating with a diverse set of stakeholders including product managers, analysts, and engineering teams. • Mentor and support other engineers, contributing to a collaborative and high-performing engineering culture. • Build a deep understanding of a complex business domain and work effectively across teams with multiple dependencies. • Own features end-to-end, from design and implementation through deployment, monitoring, and ongoing improvement.