Data Engineer

• Develop applications and data pipelines with Python
• Process large volumes of data in a distributed environment using PySpark
• Build and govern data transformations using dbt
• Orchestrate pipelines with Airflow
• Use GitHub for version control, pull requests, code reviews, and CI/CD integration
• Work with AWS services, including Lambda, CloudWatch, S3, Redshift, and Aurora
• Implement and work with Apache Iceberg
• Apply Data Lakehouse and Medallion Architecture patterns
• Execute ETL/ELT processes
• Contribute to building an innovative financial platform from the ground up, with no technical legacy

Senior Data Engineer

• Lead the centralization and evolution of the Depositary’s data platform for one of our clients
• Work with informational and transactional data, from capture across different sources through processing and structured, reliable, and resilient delivery
• Organize and evolve the different data layers using Medallion Architecture
• Centralize, integrate, and process data from multiple sources
• Query, consume, and integrate data from other systems using Kafka
• Monitor pipelines and data environments, ensuring operational reliability
• Design contingency strategies and plans to support resilience, availability, and business continuity
• Assess scenarios, identify improvements, and propose scalable, sustainable solutions
• Contribute to the architecture, integration, quality, observability, and resilience of the data ecosystem
• Evolve the platform to support future Machine Learning initiatives

Senior Data Engineer, Databricks

• Develop and maintain data ingestion pipelines from APIs, CSV, JSON, and Parquet files, as well as transactional databases
• Build integrations using native AWS services, including Glue, Lambda, S3, Kinesis, DMS, Athena, and EventBridge
• Implement incremental extraction and full-load routines with idempotency and reprocessing controls
• Monitor and address ingestion failures, including retries, dead-letter queues, and alerts
• Provision and maintain data infrastructure using Terraform
• Manage source code, CI/CD pipelines, and code reviews through GitHub, including Actions, pull requests, and branching strategies
• Apply medallion architecture across the bronze, silver, and gold layers
• Structure data in the silver layer for consumption by Analytics Engineers
• Align data contracts, schemas, and data freshness SLAs with the analytics team
• Provide technical support for troubleshooting data sources when needed

• Develop, maintain, and optimize data pipelines and ETL/ELT processes
• Perform data ingestion, transformation, and integration from multiple sources
• Work with Python, SQL, and PySpark, as well as Databricks and Apache Spark
• Develop and consume REST APIs for system integration
• Work with data from enterprise systems, including SAP/Ariba
• Support Data Quality, governance, traceability, and data reliability initiatives
• Develop automations and solutions to improve efficiency
• Support the development and enhancement of data repositories, KPIs, and analytics solutions
• Collaborate with Architecture, Engineering, Analytics, and business teams
• Build and enhance data solutions for Level Group's BPO projects

• Develop, maintain, and enhance batch and streaming data pipelines using Airflow (EKS), EMR/Spark, and dbt
• Implement data ingestion via DMS, APIs, files, and events, ensuring proper organization across Data Lake layers
• Investigate and resolve data incidents, including DAG failures, processing errors, metric inconsistencies, and schema issues
• Conduct root cause analysis (RCA) and propose improvements to prevent recurring failures
• Ensure data quality through automated tests, validations, and reconciliations
• Develop and optimize Spark and dbt transformations for performance and cost efficiency
• Optimize Athena queries and dbt models, reducing processing costs and improving response times
• Work with incremental loads (append, overwrite, merge/upsert), ensuring consistency and idempotency
• Support the evolution of the current architecture by contributing technical improvements and standardization
• Perform code reviews and follow best practices for version control and documentation
• Work closely with business teams to ensure delivered data meets functional requirements
• Monitor pipeline executions and propose continuous improvements

Mid-Level Data Engineer

• Define and evolve scalable, resilient, and efficient data architectures for analytics platforms and strategic projects.
• Establish technical standards, best practices, and data engineering guidelines across the information lifecycle.
• Design and implement data pipelines across the Bronze, Silver, and Gold layers, ensuring data quality, governance, and traceability.
• Develop and optimize data ingestion, transformation, and delivery processes in Databricks and AWS environments.
• Create and maintain connectors and integrations with various data sources, including APIs, relational databases, NoSQL systems, and enterprise applications.
• Develop reusable solutions, frameworks, and automations to increase productivity and standardize the data ecosystem.
• Implement data quality controls and frameworks, ensuring the consistency, integrity, completeness, and compliance of information.
• Monitor and optimize the performance of pipelines, queries, and distributed processing environments.
• Identify and resolve failures, bottlenecks, and structural issues in production environments.
• Establish and evolve processes for cataloging, governance, lineage, and access control for data assets.
• Evaluate new technologies, tools, and architectural approaches to continuously evolve the data platform.
• Serve as a technical reference for multidisciplinary teams, sharing knowledge and advancing data engineering practices.

Data Engineering Specialist

• Design, develop, and maintain robust, scalable, and efficient data pipelines in a GCP environment, with a focus on BigQuery
• Implement and manage data ingestion processes from ERP systems, files, and APIs
• Create, optimize, and document dimensional data models and analytical solutions within a Lakehouse architecture
• Orchestrate and monitor ETL/ELT processes using Informatica Cloud
• Partner with Analytics, Data Science, and Product teams to enable data-driven analyses and solutions
• Ensure data quality, governance, and security throughout the data lifecycle
• Apply data engineering best practices, including version control, automation, and testing
• Optimize queries and the performance of analytical environments
• Manage requests and align expectations with various stakeholders, including leadership and business teams
• Provide technical contributions to the evolution of data practices, processes, and architecture

Senior Data Engineer, GCP

• Develop and maintain scalable, efficient data pipelines (ETL/ELT)
• Handle data ingestion, transformation, and delivery in GCP environments
• Build solutions using Databricks (Spark/PySpark)
• Design and evolve data architectures (Data Lake, Data Warehouse, Lakehouse)
• Integrate diverse data sources (APIs, databases, streaming)
• Ensure data quality, governance, and reliability
• Monitor performance and optimize cloud costs
• Collaborate with BI, Analytics, and Data Science teams

Mid-Level GCP Data Engineer

• Design and develop robust, scalable, and monitorable data pipelines
• Implement data ingestion, transformation, integration, and delivery solutions
• Work with modern data architectures, including Data Lakes, Lakehouses, Data Warehouses, and Analytics platforms
• Develop solutions using Microsoft Fabric and/or AWS Databricks
• Apply Data Engineering principles regardless of the technology or platform
• Work in Microsoft Azure and AWS environments
• Contribute to decisions regarding data architecture, modeling, processing, quality, security, and governance
• Ensure the reliability, observability, performance, and maintainability of solutions
• Collaborate with BI, Data Science, Software Engineering, and business professionals
• Participate in and lead technical and alignment meetings with clients and stakeholders
• Identify opportunities for automation and the application of Artificial Intelligence
• Document solutions, technical decisions, processes, and best practices
• Take ownership of the planning, execution, and delivery of data products
• Work in a collaborative, dynamic, and delivery-oriented environment focused on high-impact data solutions

Senior Data Engineer – Microsoft Fabric

• Build and operate the backend of a Web Analytics and AI portal
• Design, implement, test, and operate backend services against latency, availability, and cost targets
• Implement typed, versioned API contracts and evolve them without breaking clients
• Own authentication, authorization, and multi-tenant access control
• Ship features end to end from API to interface when required
• Integrate the application with internal services, including AI-engineer-built agent services, handling response streaming, timeouts, cancellation, and partial failures
• Own the analytical-data read path, including data modeling, query plans, pagination, batching, caching, and consistency trade-offs
• Set and defend performance budgets; profile services, reduce tail latency, and optimize cost per request
• Run load and capacity tests before launches and use results for design decisions
• Instrument services with metrics, structured logs, and distributed tracing
• Define SLIs, SLOs, alerts, and dashboards; participate in on-call
• Diagnose and fix reliability issues; lead incident response and blameless postmortems
• Establish safe rollout practices including feature flags, canary releases, and fast rollback
• Write design docs, compare alternatives using data and prototypes, and escalate risk to the Staff Architect
• Raise the technical bar through code review, pairing, and standards for testing, observability, and safe rollout
• Use Cursor, Claude Code, and Codex as part of the workflow and review generated code against tests, types, and contracts

Data Engineer jobs in Brazil

Senior Data Engineer

Senior Data Engineer, Databricks

Senior Data Engineer

Data Engineer

Mid-Level Data Engineer

Data Engineering Specialist

Senior Data Engineer, GCP

Mid-Level GCP Data Engineer

Senior Data Engineer – Microsoft Fabric

Staff Full-Stack Engineer – Data Intensive Web-Applications, Personalization