Top Data Engineer Jobs in Sao Paulo

Own end-to-end data engineering for a Databricks-based commercial intelligence platform. Build entity resolution and reconciliation systems, evolve data models, onboard sources, and implement testing, monitoring, alerting, lineage, and incident analysis. Productionize data science models, analyze data quality and roadmap priorities, and provide technical leadership through code reviews. The role requires strong SQL, Python, PySpark, data modeling, orchestration, CI/CD, and hands-on Databricks experience.

Build and operate a governed people-data platform supporting workforce analytics and AI use cases. Design scalable data models, production pipelines, integrations, quality controls, lineage, privacy controls, and reliability practices. Own the platform’s AI-readiness and infrastructure roadmap, monitor performance, manage incidents, and partner with People Analytics, HR Technology, Security, Legal, and Enterprise Data teams. Translate approved requirements into secure, production-ready technical solutions using Workday and Azure-based data technologies.

Build and maintain secure HR data pipelines, warehouses, and data marts using dbt, Python, Snowflake, BigQuery, Airflow, and AWS. Design semantic layers and ontology models for BI and AI agents, deploy Snowflake Cortex and MCP workflows, monitor pipeline reliability, enforce data quality and PII security, and document technical processes. Collaborate remotely with distributed teams aligned to EST.

Build and maintain curated data layers for a finance data platform using SQL and Snowflake. Develop dimensional models, business transformations, Slowly Changing Dimensions, reconciliation checks, data-quality controls, and temporal data logic. Support BI consumers through governed data objects and participate in code reviews, CI/CD promotion, and automated testing. Collaborate in English with distributed engineering teams and US stakeholders.

Build and operate production data infrastructure, including batch and streaming pipelines, warehouses and lakehouses, retrieval systems, and data-quality controls. Design for schema evolution, reliability, cost, security, compliance, and observability using cloud platforms and modern orchestration tools. Partner directly with clients, explain technical trade-offs, and deploy containerized systems with CI/CD and infrastructure as code. Support AI applications through embedding, indexing, and retrieval pipelines.

Designs and supports scalable AWS Glue and PySpark data ingestion pipelines from databases, files, APIs, and streaming sources into Amazon S3. Responsibilities include medallion architecture processing, data catalog management, quality controls, reconciliation, workflow orchestration, performance and cost optimization, production support, incident resolution, and governance collaboration.

Build and maintain data pipelines and ETL processes integrating diverse first-, second-, and third-party data sources. Develop transformations, aggregations, APIs, and warehouse solutions using Snowflake and dbt. Monitor pipeline efficiency, data integrity, and storage performance while collaborating with data science, application, BI, and vendor teams. The role also recommends data architectures and supports client-facing data systems.

Build and maintain data pipelines and ETL processes integrating diverse first-, second-, and third-party data sources. Develop transformations, aggregations, APIs, and data warehouse solutions using Snowflake and dbt. Monitor pipeline efficiency, support BI tools, improve data integrity and storage efficiency, recommend architectures, and collaborate with application developers and external vendors.

Build and own scalable data architecture, canonical data models, configurable source integrations, and reliable pipelines for AI-driven financial intelligence. Use SQL, Python, dbt, cloud data warehouses, and orchestration tools to deliver idempotent, auditable data workflows. Establish lineage, data contracts, quality controls, and schema evolution practices. Lead architecture reviews, mentor distributed data engineers, and support customer-hosted multi-cloud deployments with strict tenant isolation and traceability.

Build and own production-grade data pipelines, source connectors, warehouse transformations, curated data models, reconciliation, and lineage for an AI-native finance platform. Develop dbt projects, orchestrate reliable and rerunnable workflows with Airflow or Dagster, tune warehouse performance, integrate messy enterprise systems, and ensure data correctness, auditability, and tenant isolation. Collaborate with backend and AI teams, establish data contracts, review work, and mentor engineers.

Designs, develops, and maintains large-scale Databricks data pipelines using Python, SQL, PySpark, and Delta Lake. Integrates APIs, databases, and files; applies ETL/ELT, DataOps, CI/CD, testing, governance, and data quality practices. Collaborates with business, analytics, data science, and platform teams while mentoring peers and troubleshooting performance, scalability, and reliability issues.

Builds and maintains scalable batch and streaming data pipelines across on-premises and cloud environments. Designs data models, data flows, APIs, integrations, and access methods for reporting, dashboards, operational use, and data science. Translates business requirements into technical solutions, manages code integration, applies data governance, cleanses data, and supports complex project planning and delivery. Works independently across multiple dependencies and provides coaching to junior associates.

Design, build, and scale reliable data infrastructure, including event ingestion, stream processing, data warehouses, ETL pipelines, analytics tools, and machine learning systems. Optimize performance, reliability, security, and data quality across AWS regions and data centers. Collaborate with analytics and data science teams, troubleshoot complex systems, influence architecture, conduct code reviews, and mentor data engineers while helping establish engineering best practices.

Build, maintain, monitor, and improve high-performance data pipelines across Azure and on-premises platforms. Design ETL/ELT processes, data lakes, data marts, and warehouses while ensuring performance, security, privacy, and reliability. Collaborate with BI, data science, and software engineering teams, contribute to data standards and code reviews, support incident resolution, and apply software engineering practices including Git and CI/CD. Mentor junior team members and integrate emerging data technologies.

Build secure, scalable data platform and governance automation capabilities. Develop controls for metadata, lineage, data quality, access, policy enforcement, identity, and auditability. Create secure APIs, MCP tools, enterprise integrations, automated testing and deployment, monitoring, and observability. Own production-quality code and documentation, support full lifecycle delivery, collaborate across technical and governance teams, and mentor engineers.

Design, build, deploy, and maintain AI-enabled applications for digital supply chain operations. Develop user interfaces, APIs, backend services, PostgreSQL databases, SQL queries, ETL/ELT pipelines, and integrations with BigQuery, SAP, and enterprise platforms. Implement LLMs, RAG, AI agents, and predictive models, while deploying containerized applications with Docker. Produce technical documentation and support production operations.

Lead the architecture and hands-on development of Cloudbeds’ Snowflake and dbt data platform. Own data modeling, pipelines, orchestration, governance, observability, DataOps, CI/CD, performance, cost optimization, MLOps, and AI-readiness initiatives. Mentor analytical engineers, evaluate modern tools, establish data quality and security standards, and collaborate with engineering, infrastructure, data science, and business stakeholders in a fully remote environment.

Own end-to-end AI-automated data platform migration projects (1-4 concurrent): scope, plan, execute, configure Datafold Migration Agent, run customer check-ins, manage stakeholders, partner with engineering, and improve delivery playbooks.

Owns end-to-end data engineering for ingestion, medallion architecture, curated datasets, and Power BI semantic layers. Builds scalable ETL/ELT pipelines, integrates ERP and CRM systems, implements data quality and governance controls, optimizes performance and cloud costs, and provides technical leadership. Requires Microsoft Fabric or Azure expertise, Azure Data Factory, advanced Power BI and DAX skills, data contract design, and experience with Bronze/Silver/Gold architectures.

Own and develop relational database and lakehouse data layers for higher-education applications and analytics. Build optimized SQL schemas, procedures, and models; develop Databricks, PySpark, Spark SQL, and dbt pipelines; implement governance, security, quality checks, and lineage; manage CI/CD database changes; document systems, resolve incidents, collaborate with architects and developers, and mentor mid-level engineers.

Designs, builds, and maintains production data pipelines and infrastructure supporting analytics, machine learning, and financial decision-making. Responsibilities include data ingestion, modeling, quality validation, workflow orchestration, database optimization, monitoring, troubleshooting, and managing scalable AWS or on-premises systems. The role collaborates with data scientists and analysts to ensure reliable access to large-scale and time-series financial data.

Build and optimize ETL/ELT pipelines feeding BigQuery, audit and troubleshoot data quality, and create reliable Looker Studio dashboards. The role supports sales leaders by turning raw data into accurate, actionable insights, improving reporting performance, eliminating manual processes, and ensuring data integrity across Curvion’s revenue operations ecosystem.

Design, build, schedule, monitor, and optimize scalable data pipelines using Apache Airflow and cloud and big-data technologies. Collaborate with analysts, scientists, and engineers to ensure reliable, secure data delivery. Troubleshoot failures, maintain data architecture, document technical decisions, lead solution implementation, and mentor team members through coaching, pair programming, and design reviews.

Design, develop, and optimize enterprise data pipelines and lakehouse architecture using Databricks, Spark, and Delta Lake. Build ETL/ELT processes for banking data, improve cluster and query performance, establish data quality monitoring, and support regulatory compliance. Collaborate with technical and business stakeholders, document workflows, participate in code reviews, mentor junior engineers, and contribute to continuous improvement.

Designs and delivers end-to-end data engineering solutions for enterprise clients, including production implementation, data integration, SQL optimization, cloud data platforms, automated transformation, orchestration, testing, deployment, and documentation. The role also requires client-facing communication, technical presentations, solution roadmaps, proof-of-concept development, and collaboration to ensure performance, security, scalability, and reliability.