Data Engineer

• Development and maintenance of ETL processes
• Evolution and maintenance of applications integrated with the OSS ecosystem
• Analysis, processing and integration of data from telecommunications platforms
• Development of new features and improvements in existing systems
• Integration between databases, APIs and corporate systems
• SQL query tuning and performance optimization
• Technical support for application analysis and evolution
• Technical documentation and support for internal teams
• Interaction with technical and business stakeholders

• Design and evolve data platforms using Lakehouse architecture
• Develop scalable pipelines
• Define architecture, governance, and performance standards with Databricks, Delta Lake, and PySpark
• Implement solutions on Databricks and Delta Lake
• Support technical teams and document solutions

Data Architect / Data Engineer – Databricks Senior

• Define and evolve the enterprise data architecture.
• Lead the design of conceptual, logical, and physical data models.
• Define and apply data modeling standards (relational and NoSQL), naming conventions, partitioning, and load strategies.
• Lead the implementation and evolution of Data Lake, Data Warehouse, and Lakehouse environments.
• Ensure data quality and reliability: data tests, validations, contracts/expectations, monitoring, alerts, and failure handling (DLQ, retries, idempotency).
• Define policies for data cataloging, data lineage (Data Lineage), and data security.
• Support analytical consumption: creation and maintenance of datasets and support for BI/Analytics, with attention to security.
• Promote the elimination of redundancies and inconsistencies, ensuring a single, reliable source of truth (Single Source of Truth).
• Work in partnership with business squads and cross-functional teams to turn data into strategic assets.
• Support data governance and architecture initiatives aligned with organizational needs.
• Develop and maintain ingestion and processing pipelines (batch and near real-time) for internal and external sources.
• Implement event-driven and scheduled architectures using EventBridge, Lambda, S3 events, and messaging.
• Build and optimize data layers in Amazon Redshift, ensuring performance and cost efficiency.

Senior Data Engineer

• Develop data models that support exploratory analyses, dashboards, and predictive models.
• Collaborate with Business and Data Science teams to structure analytical datasets (Data Marts, Views, Semantic Layers, Feature Store).
• Design and optimize SQL queries for high-volume data analysis.
• Document applied business rules.
• Explore data to identify patterns, anomalies, and opportunities for process improvements.
• Implement data governance and quality best practices (lineage, catalog, consistency).
• Support product, marketing, and business teams in understanding data and enabling ad-hoc analyses.

• Develop and support data integration solutions using Oracle Data Integrator (ODI);
• Provide production support and maintenance, performing incident analysis and resolution;
• Develop, maintain and optimize ETL processes;
• Create and optimize SQL and PL/SQL queries;
• Ensure data quality, integrity and availability;
• Collaborate with cross-functional teams in an agile environment;
• Support continuous improvement initiatives and the evolution of data solutions.

Senior Data Engineer – DB Developer, Oracle Data Integrator

• Design, develop, and maintain robust, scalable, high-performance data pipelines
• Implement and optimize data models and structures on the Snowflake platform, ensuring efficiency, governance, and security
• Build data ingestion, transformation, and integration processes using tools such as dbt, Snowpipe, Embulk, Meltano, or similar
• Ensure data quality through testing, validations, and continuous pipeline monitoring
• Optimize consumption, storage, and performance in Snowflake (warehouses, clustering, caching, micro-partitioning, access control)
• Support data architecture initiatives and the evolution of the analytics platform
• Implement effective monitoring and observability metrics and processes
• Expose data to end users via tools such as Power BI, Azure API Apps, or other modern visualization platforms

• Atuar no desenvolvimento e implementação de soluções de gerenciamento, processamento e análise de grandes massas de dados.
• Essas soluções permitirão tanto a alimentação de modelos de Ciência de Dados e de Inteligência Artificial quanto a análise direta dos dados pelos usuários de negócio.
• Para tal, será necessário interagir fortemente dentro dos times de Ciência de Dados e Analytics e com os usuários de negócio, apoiando-os nas análises.
• As soluções envolvem o tratamento de grandes massas de dados, a integração de fontes variadas de dados e/ou tratamento de dados com grande velocidade, visando a criação de vantagens competitivas para a Companhia apoiadas no uso intensivo de dados.

Engenheiro de Dados Pleno

• Design, develop and maintain scalable, resilient data pipelines.
• Model, structure and optimize databases for analytics and business applications.
• Develop data ingestion, transformation and delivery processes (ETL/ELT).
• Build and maintain data architectures in cloud environments, focusing on performance, security and governance.
• Identify opportunities to optimize data platform costs and performance.
• Prepare technical documentation, process flows and solution architecture.
• Design integration processes between internal and external systems.
• Gather technical requirements together with business stakeholders.
• Support data scientists, analysts and developers in using the data platforms.
• Participate in defining engineering, quality and data governance standards.
• Manage technical data projects, tracking schedules, risks, dependencies and deliverables.
• Lead technical meetings with internal teams and vendors when necessary.

• Projetar e evoluir a arquitetura de dados corporativa, garantindo escalabilidade, performance, segurança e integridade dos dados;
• Atuar em conjunto com arquitetos de software para alinhar os modelos de dados das aplicações às necessidades analíticas e regulatórias da empresa;
• Definir padrões e boas práticas para modelagem de dados (OLTP e OLAP), integração de dados e documentação técnica;
• Liderar iniciativas de modernização do ecossistema de dados, como data mesh, data lakehouse, event streaming e outros;
• Apoiar os times de Engenharia de dados na definição de pipelines, ingestão e transformação de dados, bem como em estratégias de particionamento, versionamento e catalogação;
• Ser referência técnica em decisões estratégicas relacionadas a arquitetura de dados e integração entre domínios de negócio;
• Atuar nas squads de desenvolvimento de produtos digitais trazendo uma visão sobre arquitetura de dados;
• Executar outras atividades correlatas.

Arquiteto de Dados I – Estratégia de Dados

• Ensure monitoring of Data Quality;
• Implement processes to monitor data accuracy, consistency, and integrity;
• Data Security: Protect data against unauthorized access through security measures such as encryption and access control;
• Collaboration with Teams: Work closely with data professionals and business areas to understand their needs;
• Monitoring and Optimization: Monitor the performance of the data platform and implement improvements to optimize efficiency and scalability;
• Documentation: Document data engineering processes and procedures to ensure practices are reproducible and understandable for other team members;

• Projetar, desenvolver e manter pipelines de dados escaláveis e eficientes usando PySpark no Databricks.
• Integrar e processar dados utilizando Apache Kafka.
• Colaborar com cientistas de dados, analistas e engenheiros de software para garantir a qualidade e disponibilidade dos dados.
• Garantir boas práticas de versionamento, testes e documentação de código.
• Monitorar e otimizar o desempenho de pipelines e jobs de dados.
• Atuar na governança e segurança dos dados em conformidade com políticas internas e regulatórias.

• Design and implement efficient, scalable data pipelines aligned with business needs.
• Develop and maintain data ingestion and transformation pipelines in Google Cloud Platform (GCP) environments.
• Build solutions for Data Lake, Data Warehouse and Data Mart.
• Define and apply relational and dimensional data modeling standards with a focus on performance, scalability and usability.
• Translate business requirements into clear, consistent and sustainable data models.
• Support data consumption through Business Intelligence tools, ensuring semantic quality and query performance.
• Ensure data quality, governance, security and compliance throughout the data lifecycle.
• Monitor and optimize cloud environment costs and performance.
• Collaborate with multidisciplinary teams to promote data engineering and architecture best practices.

Data Engineer – Databricks

• Translate business requirements into technical solutions.
• Analyze current business practices and identify future opportunities.
• Support the planning and implementation of data design services.
• Deliver architectures for transformations and modernization of data solutions.
• Design and build modern data pipelines and data flows.
• Develop and maintain data warehouse schemas and databases.
• Expose data to end users using modern visualization platforms.

• Technical leadership within the squad, responsible for managing the tribe’s technical data development
• Accountable for the quality of the team’s deliverables
• People management (feedback, 1:1s, career development and allocation mapping)
• Management of third parties (access, payments and performance)
• Monitoring critical data metrics and data products to ensure their stability and sustainability
• Tracking the tribe’s cloud costs
• Leading and ensuring execution of agile ceremonies
• Technical management of the backlog, including technical debt
• Acting as a Federated Architect: designing technical solutions and participating in architecture and data modeling discussions
• Participating in the operational model rituals to ensure synergy among the different data roles
• Working within a directorate/area that is highly strategic to the business.

Data Engineering Coordinator

• Support Snowflake-to-Databricks data migration activities
• Design and implement scalable data pipelines in Snowflake and Databricks
• Develop data ingestion and transformation processes from various sources
• Migrate and optimize existing data workflows, ETL processes, and transformation logic
• Build and maintain data transformations using SQL, Python, dbt, and other data engineering tools
• Work with Databricks technologies, including Apache Spark-based data processing
• Optimize pipelines for performance, scalability, cost efficiency, and maintainability
• Automate data workflows and orchestration processes using tools such as Airflow or similar technologies
• Collaborate with technical and business stakeholders to understand requirements and deliver effective data solutions
• Ensure data quality and reliability throughout migration and development processes

Data Engineer – Databricks, Snowflake, Spanish-speaking

• Develop, maintain and optimize data pipelines using Python.
• Design, build and maintain ETL/ELT processes.
• Develop integration solutions between different data sources.
• Model and structure data for Data Lake and Data Warehouse.
• Work on ingestion, transformation and provisioning of data in an AWS environment.
• Monitor and optimize performance of data pipelines and data processes.
• Implement data engineering best practices, version control and code quality standards.
• Collaborate with Analytics, BI, Data Science and Development teams.
• Participate in defining the architecture of cloud data solutions.
• Support incident resolution and continuous improvement of the data platforms.

Data Engineer, Mid/Senior

• Desenvolver e manter pipelines de dados em ambientes batch e streaming utilizando Kafka, Spark e Airflow
• Integrar diferentes fontes de dados, assegurando qualidade, consistência e integridade das informações
• Orquestrar workflows e automatizar processos de engenharia de dados
• Implementar e evoluir esteiras de CI/CD para pipelines de dados
• Garantir boas práticas de segurança, governança, monitoramento e observabilidade do ambiente de dados
• Atuar em arquitetura e soluções em nuvem AWS, contribuindo para a evolução contínua da plataforma

• Lead the data dimension of the migration for the SAP SQL Anywhere layer (~3.5M rows; 8,946 triggers; 805 stored procedures; 40k+ access points)
• Evaluate and deploy a change data capture mechanism to enable coexistence stitching (no managed CDC for SQL Anywhere; dbtran is not CDC)
• Ensure single-writer ownership during the dual-run
• Protect data integrity and performance of the fiscal ledger system
• Maintain the data side of the equivalence harness (production history replay)
• Produce masked/synthetic datasets in compliance with Brazil's LGPD
• Define the migration and reconciliation plan in waves, avoiding split-brain
• Extract and document embedded business logic as reusable artifacts

Senior DBA – Legacy Data Migration

• Lead the design of modern data architectures (Lakehouse, Data Mesh, Medallion, etc.) for analytical and operational environments
• Collaborate with engineering, BI, Data Science, and Generative AI teams to ensure technical cohesion and alignment with business objectives
• Define standards for data modeling, ingestion, processing, and governance
• Support the construction of scalable pipelines using DataOps and CI/CD practices
• Make technical decisions based on hands-on data engineering experience
• Conduct technical reviews and advise on tool selection and best practices
• Engage with business stakeholders and clients' executive teams to recommend fit-for-purpose and sustainable solutions.

Senior Data Architect

• Desenvolver e manter pipelines de dados em ambiente GCP
• Projetar e implementar modelos de dados analíticos e operacionais
• Desenvolver APIs e serviços para consumo e disponibilização de dados
• Implementar soluções utilizando Python para processamento e transformação de dados
• Trabalhar com bancos de dados distribuídos, especialmente Google Cloud Spanner e BigQuery
• Construir soluções escaláveis, resilientes e de alta disponibilidade
• Participar de definições de arquitetura e decisões técnicas
• Garantir qualidade, governança e observabilidade das soluções de dados
• Atuar próximo às áreas de negócio para traduzir requisitos em soluções técnicas
• Realizar code reviews e apoiar tecnicamente outros membros da equipe

Data Engineer jobs in Brazil

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Data Engineer

Data Architect / Data Engineer – Databricks Senior

Senior Data Engineer

Senior Data Engineer

Senior Data Engineer – DB Developer, Oracle Data Integrator

Senior Data Engineer

Engenheiro de Dados Pleno

Senior Data Engineer

Arquiteto de Dados I – Estratégia de Dados

Data Engineer

Engenheiro de Dados Pleno

Data Engineer – Databricks

Senior Data Engineer

Data Engineering Coordinator

Data Engineer – Databricks, Snowflake, Spanish-speaking

Data Engineer, Mid/Senior

Engenheiro de Dados Pleno

Senior DBA – Legacy Data Migration

Senior Data Architect

Senior Engenheiro de Dados