Data Engineer

• Participate in the technical refinement of data cards, helping to map sources, transformation rules and destinations before development.
• Develop and maintain ETL/ELT pipelines using Python and SQL.
• Implement data capture strategies: full load, incremental by timestamp and Change Data Capture (CDC).
• Build and organize data into Raw + Trusted + Refined layers, respecting data contracts and governance standards.
• Integrate flows into pipeline orchestration (DAGs).
• Perform data quality validations: volume checks, nulls and duplicates before deployment.
• Document data flows, architectural decisions and system dependencies.

Junior Data Engineer

• Data Integration and Collection: Aggregates structured, semi-structured and unstructured data from multiple sources (databases, APIs, files);
• Real-time and Batch Data Ingestion: Designs scalable ingestion architectures using Pub/Sub for streaming and Cloud Storage/Dataflow for batch processing, ensuring data integrity in BigQuery;
• Data Processing and Transformation: Builds pipelines to transform raw data with standardization, deduplication, filtering and enrichment processes;
• Governance and Operations: Manages orchestration via Cloud Composer with a focus on idempotency and resilience, implements proactive monitoring (logs/alerts) and technical documentation to ensure continuity and maintainability of solutions;
• Performance Optimization: Leads FinOps initiatives, optimizing BigQuery slot utilization, storage costs and job efficiency in Dataflow/Dataproc to reduce TCO (Total Cost of Ownership);

Senior Data Engineer

• Develop, maintain, and optimize data ingestion, processing, and transformation pipelines.
• Work on structuring and managing Bronze, Silver, and Gold data layers.
• Implement data integration and data provisioning processes using Denodo.
• Work on data modeling for analytical and enterprise environments.
• Ensure data quality, integrity, and governance.
• Support business and technology teams in building data-driven solutions.
• Monitor and optimize the performance of loads and queries in BigQuery environments.
• Participate in architectural decisions and best practices related to data engineering.

Mid-level Data Engineer, GCP

• Build and support data pipelines that can handle the volume and complexity of data while ensuring scale, data accuracy, availability, observability, security, and optimum performance
• Developing and maintaining data warehouse tables, views, and models, for consumption by analysts and downstream applications
• Own and lead workstreams while driving results through self or your team
• Participate in periodic data engineering activities, e.g. monthly insights reporting, profile data updates, etc
• Troubleshoot production issues, and participating in on-call activities
• Identify several areas for improving data engineering processes, and share with the team
• Contribute consistently towards building our data platform, which includes data pipelines, and data warehouse layers
• Independently own and lead workstreams whether it is periodic data engineering activities, or work items in support of our roadmap
• Deepen your understanding, and build subject matter expertise of our data & ecosystem
• Your contributions have led to us making significant progress in implementing the data platform strategy, and key data initiatives to support the company's growth
• You’ve established yourself as a key team member with subject matter expertise within data engineering

• Organizar, coletar e processar (ferramentas ETL) grandes volumes de dados, traduzindo os objetivos de negócios do cliente em uma estratégia de gerenciamento de informações e inteligência de negócios.
• Responsável por manter os pipelines de dados e garantir a segurança no acesso as informações.
• Sua atuação pode abranger a criação e exposição de métricas (painéis) sobre os dados obtidos, garantindo assim qualidade, e integridade a estas informações.

Engenheiro de Dados Sênior

• Responsible for the maintenance, deployment and evolution of Big Data solutions, working in environments that process large volumes of data.
• Ensure the performance, reliability and scalability of applications, as well as support consumers of data solutions and collaborate with multidisciplinary teams in an agile environment.
• Monitor deployments and provide support to consumers of data solutions.
• Develop and maintain large-scale data processing pipelines.
• Identify and resolve issues related to application performance and reliability.
• Implement and optimize solutions using Big Data technologies.
• Participate in defining architectures and strategies for data processing and storage.
• Work together with multidisciplinary teams to deliver innovative solutions.
• Support continuous improvement initiatives and process automation.
• Operate following agile methodologies, participating in Scrum ceremonies.

Data Engineer, Big Data – Senior

• You will architect and evolve the datalake that is the company's data nervous system — the foundation that feeds, in real time, the dynamic pricing engine, ML models, and the group's business intelligence.
• This is an ownership role: you define the multi-tenant Lakehouse architecture, from streaming to the semantic layer, and are responsible for its reliability, governance, and cost.
• Design and evolve the data lake on Apache Iceberg over S3 — well-defined layers, partitioning and compaction, time-travel and support for DELETE/UPDATE for LGPD (Brazilian data protection law).
• Build real-time ingestion (Kafka, Flink, CDC with Debezium) with controlled schema evolution (Schema Registry) and delivery guarantees.
• Model the transformation layer in dbt and orchestrate batch and quality flows in Airflow, from crawler to backfill.
• Maintain metric definitions in Cube.js — the single source that feeds BI and AI agents and ensures consistency across the company.
• Operate federated and low-latency OLAP queries over the lake, with cost and access isolation by tenant and performant queries.
• Ensure data testing, lineage and cost efficiency, keeping the platform reliable as it scales.

• Develop and maintain data ingestion, transformation, and publishing pipelines (ETL/ELT).
• Integrate data from different systems, formats, and domains.
• Implement versioning, lineage, and data validation mechanisms.
• Ensure performance, monitoring, reliability, and cost control of data flows.
• Automate processes and support the continuous operation of data environments.
• Support data science and AI teams by providing analysis-ready data.
• Document pipelines and support knowledge transfer.

• Develop data ingestion pipelines in Azure Data Factory and implement transformations in Databricks/DBT, ensuring consistency and traceability.
• Migrate data flows from legacy architectures to the current layered architecture (bronze, silver and gold).
• Monitor and maintain existing pipelines, troubleshooting failures in Azure Data Factory, Databricks and Synapse to prevent disruptions to critical processes. Implement data quality routines (integrity, completeness, accuracy) and record evidence.
• Maintain and evolve data flows for specific business areas (e.g., commercial stockouts), ensuring delivery of strategic indicators.
• Create and maintain technical documentation for pipelines, flows and integrations in the Azure DevOps Wiki, Visio and OneNote.
• Log activities in Azure DevOps, supporting time tracking and demand monitoring.
• Map business processes (AS IS / TO BE), supporting data-related digital transformation initiatives.

Data Engineering Analyst II

• Design, develop and optimize complex data ingestion and transformation pipelines across multiple layers of the Data Lake (bronze, silver, gold) using Azure Data Factory, Databricks and DBT.
• Define technical standards for data architecture, ensuring governance, scalability and alignment with Solar's needs.
• Implement automation and continuous integration (CI/CD) processes for data workflows.
• Monitor pipeline performance, performing advanced tuning on queries and distributed processes (Spark, Databricks).
• Ensure availability and quality of critical data used by strategic business areas, Analytics, BI and Data Science.
• Perform root cause analyses on pipeline failures and propose structural solutions for recurring issues.
• Document architectures, pipelines and best practices in Azure DevOps Wiki, Visio and other knowledge repositories.
• Act as a technical reference, supporting and mentoring Analysts at levels I and II.
• Collaborate with business areas to translate requirements into technical data solutions.

Data Engineer III

• Develop, maintain and optimize data pipelines (ETL/ELT) in cloud environments
• Work on data modeling and structuring of databases for analytical consumption
• Ensure data quality, consistency and traceability
• Support data integrations via REST APIs and cloud services
• Work with Google Cloud Platform (GCP), especially BigQuery
• Support data migration initiatives to cloud environments (BigQuery or Microsoft Fabric)
• Contribute to the evolution of data processes, performance and technical best practices
• Collaborate with technical and business teams to understand requirements

Senior GCP Data Engineer

• Translate business requirements into data engineering problems and solutions.
• Design, develop and implement scalable, resilient data pipelines.
• Build and maintain data ingestion, transformation, and provisioning processes.
• Ensure the quality, performance, and governance of implemented solutions.
• Participate in defining data engineering architectures and standards.
• Work closely with cross-functional teams to support data-driven initiatives.
• Perform troubleshooting and optimization of data processes and environments.

Mid-level Data Engineer

• Translate business requirements into data engineering problems and solutions.
• Design, develop, and implement scalable and resilient data pipelines.
• Build and maintain data ingestion, transformation, and delivery processes.
• Ensure the quality, performance, and governance of implemented solutions.
• Participate in defining data engineering architectures and standards.
• Collaborate with multidisciplinary teams, supporting data-driven initiatives.
• Perform troubleshooting and optimization of data processes and environments.
• Act as a technical reference for the team, supporting architectural decisions and best practices.
• Mentor less senior professionals, promoting the technical growth of the team.
• Drive the design and evolution of data engineering solutions aligned with business needs.

• Design, develop, and maintain ETL/ELT data pipelines
• Build and optimize data architectures in cloud environments
• Ensure data quality, integrity, and governance
• Develop and maintain analytical and operational data models
• Collaborate with business teams, analysts, and data scientists
• Monitor and optimize pipeline performance and cost
• Handle ingestion and processing of large volumes of data
• Support the definition of data best practices and standards
• Participate in incident resolution and continuous improvement of data solutions

• Design, develop, and implement robust data pipelines, connecting data from various sources primarily using tools from the AWS ecosystem;
• Implement scalable and efficient data enrichment solutions using DBT to transform raw data into custom data models and actionable business insights;
• Manage integrations, transformation processes, and task automations using data connectors, SQL, Python, and REST APIs;
• Use SQL to query and manipulate data in relational and non-relational databases;
• Apply Spark to process large volumes of data in real time and in batch.

Data Engineering Specialist

• Perform data modeling, working with technologies such as Databricks, SAP Datasphere, Azure Data Factory, Azure Data Lake Storage (ADLS), Python, and databases (SQL Server, Oracle);
• Work on extracting data from files received monthly, building a data lake and tables;
• Structure RAG for the client's debt contracts and other documents related to loan agreements;
• Perform ETL from CXL APIs and transform and process them into Databricks databases to feed CXL quotes;
• Structure data tables in Databricks for Judicial Deposits, Financial Guarantees and Collateral;
• Develop ETL processes for analyzing structured and unstructured data, using the client's chat API to analyze files and validate data;
• Build the Position Manager data product and create reports to replace CXL's Crystal Reports;

Senior Data Engineer – Databricks, ADF, AI

• Define and evolve data architectures, ensuring scalability, governance and efficiency of solutions;
• Establish standards, best practices and technical guidelines for the data ecosystem;
• Provide technical support to engineering teams during implementation of proposed solutions;
• Translate business needs into structured, sustainable data solutions;
• Participate in technical discussions with clients and stakeholders with clarity and depth;
• Evaluate and propose solutions in cloud environments, considering performance, cost and security;
• Work on integrating data from different systems, including SAP and operational systems.

Data Architect – Data Governance

• Projetar, desenvolver e evoluir **Produtos de Dados** que suportem decisões estratégicas e iniciativas de transformação digital;
• Construir soluções analíticas modernas utilizando a plataforma **Snowflake** como principal ecossistema de dados;
• Desenvolver pipelines de dados escaláveis, confiáveis e preparados para ambientes multi-cloud;
• Explorar e implementar soluções aceleradoras utilizando **IA Generativa, LLMs e recursos nativos de IA da plataforma;**
• Participar da definição de arquiteturas modernas de dados e analytics;
• Atuar próximo às áreas de negócio para entender necessidades, identificar oportunidades e transformar requisitos em soluções;
• Trabalhar na criação e evolução de ativos reutilizáveis, frameworks e componentes que acelerem a entrega de novos produtos;
• Garantir boas práticas de qualidade, governança, observabilidade e performance dos dados;
• Colaborar com arquitetos, cientistas de dados, engenheiros e stakeholders de negócio para criar soluções integradas e escaláveis.

Engenheiro de Dados Sênior – Snowflake

• Responsible for understanding, preparing, transforming, loading, and validating data migrated from the legacy system to the new model.
• Map entities, fields, and relationships of the current model, including user, subscription, dependent, and payment where applicable to the scope.
• Perform AS IS → TO BE mapping, identifying gaps, inconsistencies, duplicates, and required rules for the new data model.
• Define and execute processes for extraction, cleansing, normalization, transformation, and loading of legacy Filó data.
• Create import scripts, integrity controls, execution logs, volume validations, and data reconciliation.
• Support modeling of the partner, company, beneficiary, dependents, offers, and subscriptions hierarchy.
• Participate in cutover strategy, data freeze, migration window, and rollback planning.

• Define and implement Artificial Intelligence solutions applied to data modernization and legacy systems.
• Develop mechanisms for analyzing, interpreting, and extracting technical information from legacy artifacts.
• Build Generative AI–based solutions to accelerate documentation, transformation, and migration processes.
• Create intermediate metadata models to represent flows, business rules, dependencies, entities, and transformations.
• Develop accelerators and reusable components aligned with the enterprise data architecture.
• Support the definition of templates, technical standards, and declarative structures for modern pipelines.
• Develop and evolve pipelines using Databricks, PySpark, Lakeflow Jobs, and Declarative Pipelines.
• Work with batch loads, incremental ingestions, CDC (Change Data Capture), and enterprise integrations.
• Support the advancement of data governance, traceability, and data quality during migration.
• Collaborate with architects, data engineers, and platform specialists to define scalable and secure solutions.

See all jobs on JobTailor

Junior Data Engineer

Senior Data Engineer

Mid-level Data Engineer, GCP

Senior Data Engineer

Engenheiro de Dados Sênior

Data Engineer, Big Data – Senior

Senior Data Engineer

Senior Data Engineer

Data Engineering Analyst II

Data Engineer III

Senior GCP Data Engineer

Mid-level Data Engineer

Senior Data Engineer

Senior Data Engineer

Data Engineering Specialist

Senior Data Engineer – Databricks, ADF, AI

Data Architect – Data Governance

Engenheiro de Dados Sênior – Snowflake

Senior Data Engineer

Data Engineer – Specialist