Top Data Engineer Jobs in Sao Paulo

Design, build, schedule, monitor, and optimize scalable data pipelines using Apache Airflow. Maintain reliable and secure data architecture, troubleshoot failures, document technical decisions, and provide technical leadership. Collaborate with analysts, data scientists, and engineers while mentoring team members through design reviews, pair programming, and knowledge sharing.

Design, develop, and optimize scalable data pipelines using Databricks, Apache Spark, SQL, Python, Scala, and Azure. Process large datasets, improve pipeline performance and reliability, implement data quality and governance practices, troubleshoot complex issues, document solutions, and collaborate with technical teams, data professionals, business stakeholders, and clients.

Design, build and own end-to-end data solutions and infrastructure. Implement scalable pipelines using cloud and big-data technologies, optimize performance, create REST APIs and CI/CD, coach teammates, and communicate technical solutions to business stakeholders.

Designs and evolves scalable data pipelines, warehouses, lakes, and streaming architectures. Builds ETL/ELT processes, ensures data quality and security, optimizes database performance, and translates complex requirements into technical solutions. Collaborates with data scientists and stakeholders on modeling while mentoring junior engineers. The role requires modern data stack, cloud, programming, data modeling, communication, and client-facing nearshore or offshore experience.

Designs and optimizes semantic data models, analytical views, and production ETL/ELT pipelines for AI and analytics workloads. Tunes databases for low-latency retrieval, implements RBAC, row-level security, data masking, governance, monitoring, and quality standards, and collaborates with AI engineering teams on RAG and agentic AI systems.

Build and maintain BI dashboards, reports, data models, and governed data marts using Looker Studio, Power BI, SQL, BigQuery, Snowflake, and dbt. Analyze data for trends and insights, troubleshoot pipelines, support cloud migration validation, and improve data quality. Collaborate with engineering and business stakeholders while exploring agentic AI and predictive analytics. Manage reporting end to end, including requirements, modeling, validation, governance, and stakeholder presentations.

Design and maintain relational and document-based data architectures, including MS SQL Server and MongoDB schemas, complex queries, stored procedures, indexing, and performance tuning. Build secure data pipelines and APIs, deploy schemas through automated CI/CD pipelines, resolve data quality issues, and support production software. Develop testing strategies and automation while collaborating in English across LATAM and Central Time teams.

Build and maintain production data pipelines and models using SQL, Python, dbt, Airflow, and BigQuery. Ensure data quality, optimize databases and dashboards, deploy datasets and reporting products, and document data systems. Collaborate with analysts and stakeholders, mentor junior engineers, apply software engineering best practices, and own complex data solutions through delivery. The role requires agile collaboration, technical leadership, and regular performance audits.

Build and operate reliable data infrastructure for scientific and operational use. Responsibilities include developing ingestion pipelines, orchestrated workflows, warehouse data models, integrations, testing, monitoring, alerting, documentation, and data quality investigations. The role collaborates with scientists and engineers to productionize datasets for geospatial and statistical analysis and improve data lineage, permissions, schemas, and self-service access.

We're seeking a Data Engineer with 5+ years of experience to build data processing solutions using Python, design data models, and process large data streams while ensuring high performance across various data technologies.

Design, build, and maintain scalable data pipelines and architectures; ensure data quality and reliability; enable self-service analytics; collaborate with Data Science, Data Ops, and product teams; troubleshoot and optimize data systems; document and improve data workflows and infrastructure.

Build and scale Klar’s data platform, including data lakes, lakehouses, streaming and batch pipelines, and real-time ingestion. Design AWS-based infrastructure using Iceberg, S3, Glue, Redshift, Athena, and Kafka. Establish data quality, governance, monitoring, and reliability practices while partnering with engineering, analytics, and business teams on AI/ML, fraud detection, reporting, reconciliations, and regulatory use cases.

Build and maintain data models, ETL/ELT pipelines, and reliable streaming integrations for high-volume telecom and customer data. Develop transformations in dbt, orchestrate workflows with Airflow, write optimized SQL, and implement data quality, monitoring, documentation, and reconciliation processes. The role partners with the Central Data team to deliver accurate, timely, traceable datasets for reporting and self-service analytics.

Builds the data and knowledge infrastructure powering AI agents, including production RAG pipelines, embeddings, vector search, reranking, ingestion, ETL/ELT, document processing, and knowledge architecture. Integrates Salesforce, ServiceNow, enterprise repositories, PostgreSQL, vector stores, and Snowflake while ensuring data quality, security, lineage, scalability, and retrieval performance. Develops multi-tenant indexing and feedback systems and collaborates with GenAI engineers in a remote Brazil-based role requiring fluent English and occasional travel.

Design and optimise Snowflake architectures and customer data foundations to support segmentation, personalisation, and audience activation. Build pipelines and reverse-ETL flows to Braze, Hightouch, and CDPs, implement identity resolution and consent frameworks, ensure data quality and governance, and collaborate with CRM, analytics, and marketing teams to enable multi-channel customer engagement and scalable customer 360 solutions.

Designs and maintains scalable, secure data platforms and end-to-end pipelines. Builds data models, transformations, quality controls, self-service datasets, and observability practices. Troubleshoots data issues, optimizes SQL and platform performance, evaluates technologies through proofs of concept, and partners with stakeholders on scalable solutions. The role also contributes to technical strategy, data governance, engineering standards, and data-driven culture while serving as a technical reference for the data engineering team.

Build and optimize Snowflake ELT/ETL pipelines and dbt-based semantic models for a multi-country cloud analytics platform. Implement automated data quality testing, Git-based version control, and CI/CD workflows. Monitor ingestion and transformation pipelines, resolve operational incidents, collaborate with BI, data science, and business teams, and maintain documentation, data dictionaries, lineage, and cataloging standards.

Build and operate scalable batch and streaming data pipelines powering AI systems. Own ingestion, storage, transformation, serving, monitoring, validation, and continuous improvement across the data lifecycle. Partner with AI engineers and researchers to translate model requirements into reliable production infrastructure, using data lakes, warehouses, relational, graph, and vector databases. Define data strategy and ensure high-quality, observable, timely data for AI models and agents.

Build and maintain scalable batch and streaming data pipelines, develop and optimize data models in a cloud warehouse, implement ELT/ETL and orchestration (Airflow/Dagster), ensure data quality and monitoring, handle sensitive data with HIPAA-aligned practices, and evolve the platform's data architecture while partnering with analytics, product, and engineering teams.

Build and maintain end-to-end data pipelines, models, data marts, and semantic layers for marketing, client, BI, product, and AI consumers. Integrate fragmented advertising and customer data across multiple platforms and tenants, manage dbt projects, ensure data quality, optimize performance, and support client-specific modeling. Use AI-assisted development workflows, collaborate cross-functionally, and deliver reliable, AI-ready data products.

Build and register domain-grounded data agents over governed Databricks and Snowflake datasets. Lead lakehouse migrations to open formats such as Apache Iceberg, curate catalogue metadata, and support data-contract workflows. Partner with business stakeholders and subject-matter experts to onboard commercial, finance, R&D, and real-world data domains. Ensure data quality, observability, lineage, governed access, masking, and row- and column-level security while enabling reliable AI-agent access to enterprise data.

Own image and video dataset lifecycle and quality, build and maintain data pipelines and versioning, create internal tools for labeling and curation, coordinate with labeling teams, and improve training-data collection using model-assisted and active-learning approaches.

Work with cross-functional teams to design, build, and deploy end-to-end data solutions. Own conception and implementation of data infrastructure, pipelines, ML model integration, REST APIs, and CI/CD. Collaborate, train, and present findings; stay current on cloud and big-data technologies.

Designs, builds, and maintains scalable AWS and Snowflake data platforms, pipelines, integrations, data models, and quality controls. Owns production workflows, including SAS-based healthcare claims processing, while improving monitoring, resilience, security, governance, performance, and cost efficiency. Collaborates with clients to deliver analytics and AI-ready datasets, modernize legacy processes, document solutions, and support reliable data operations.

As a Data Engineer, you'll design and maintain data models and pipelines, collaborate with teams, and ensure high-quality data for analytics.