Top Data Engineer Jobs in Sao Paulo

The Data Engineer will migrate ETL jobs from Talend to Snowflake, validating and documenting the process, while building data quality frameworks and diagnostic tools.

Own the enterprise data platform roadmap and architecture. Build and modernize scalable, serverless data pipelines using Airflow, dbt, Python, and Fivetran. Design and optimize Redshift-based data models and Sigma analytics, implement observability and automated testing, and mentor engineers while driving data governance cross-functionally.

As a Software Engineer for Data Infrastructure at Speechify, you will manage data collection, enhance cloud infrastructure, and collaborate with researchers to optimize datasets for AI models.

The Data Engineer will design and implement scalable data platforms, build data pipelines, collaborate with teams to ensure data quality, and streamline processes using AWS services and big data technologies.

As a Data Engineer, you'll maintain and enhance a data ingestion pipeline, collaborate with data scientists and engineers, and implement features for efficiency and scalability.

The Senior Data Engineer will design, implement, and optimize data pipelines and processes, ensuring data access and quality across systems while collaborating with backend and analytics teams.

Design, build, and operate Avra’s data platform services, ingestion pipelines, streaming integrations, and cloud-native deployments. Collaborate across AI, product, and infrastructure teams to ensure reliability, observability, and scalability of data products and APIs that power Foundation AI and customer-facing experiences.

Lead design and delivery of modular, automated, event-driven data pipelines and data models on Google Cloud. Build resilient, testable ETL/ELT processes, embed quality-by-design, coach junior engineers, collaborate with engineering and QA, and ensure data governance and privacy alignment while driving DevOps and IaC adoption across the Data function.

Lead the design and development of scalable data acquisition systems, architect platform components, and provide technical leadership while ensuring data integrity and high performance.

Design, build, and optimize SQL Server-based data layers and Azure data solutions. Own query performance, indexing, concurrency, CDC pipelines, ORM data access patterns (C#), and database CI/CD. Partner with engineers and architects, mentor team members, and ensure scalable, low-latency data platforms.

Own technical delivery and reliability of cloud-native data platform environments (AWS/Azure) for managed services clients. Build and maintain IaC and CI/CD pipelines, monitor and troubleshoot ETL/ELT jobs, provide technical leadership, mentor junior engineers, and contribute reusable templates, runbooks, and operational playbooks to standardize platform delivery.

Lead Data Engineer responsible for designing and implementing PySpark/Python data pipelines, ETL processes, and data warehousing solutions. Collaborate with ML engineers, software devs, analytics engineers, and product managers to deliver highly available data solutions. Work remotely on a nearshore basis (Central/South America), following US client schedules and onboarding requirements.

The Senior Data Engineer will develop end-to-end technical data solutions, ensuring performance and security while integrating data across cloud platforms like Snowflake and AWS, and optimizing SQL queries.

Seeking a skilled Data Engineer with Apache Airflow experience to design, build, and maintain scalable data pipelines, optimize ETL processes, and provide technical leadership while collaborating with cross-functional teams.

Build and deploy Databricks Lakehouse pipelines on AWS to ingest, parse, and harmonize scientific instrument data for biopharma research. Prototype data acquisition, implement file parsers, design data models, develop Python ETL/ELT pipelines and tests, and work directly with customers to validate solutions.

Design and develop software for edge devices, cloud servers, and web applications, focusing on APIs, user interfaces, and data processing pipelines while collaborating with teams to solve technical issues.

Responsible for developing and scaling image capture and analysis software, building APIs, enhancing user interfaces, and optimizing data processing and machine learning pipelines.

Design, build, and maintain complex datasets, ETL pipelines, and data warehouse solutions; implement automated data extraction, transformation, and loading; create scripts and tools; monitor and resolve data issues; collaborate with data scientists, architects, and business teams to improve data models and reporting; provide technical estimates and automate incident detection.

The Data Engineer will implement ETL/ELT solutions, design data pipelines, ensure data security, and optimize data processing systems. Responsibilities include data transformation and compliance collaboration.

Design and execute cloud data migration workflows across AWS/GCP/Azure, build and maintain ETL/ELT pipelines, integrate and evaluate LLM/generative AI components, perform system testing and validation, ensure data integrity and performance, document findings, and collaborate with cross-functional teams.

Define and implement architectural solutions for data pipelines, drive best practices for ETL workflows, and lead client engagement in a consulting environment.

Design, build, and maintain Azure-native data pipelines and lakehouse layers. Ingest from APIs, IoT, ERP and files; develop Databricks PySpark jobs and Synapse workloads; ensure data quality, security, CI/CD, and governance while collaborating with analytics consumers.

The Senior Data Engineer designs and maintains data pipelines and systems, collaborates with teams, and optimizes data workflows using cloud technologies.

Lead architecture and scaling of the enterprise data platform. Build and modernize serverless, production-grade data pipelines with Airflow, dbt, Python, and AWS. Integrate Fivetran, Redshift, and Sigma, implement observability/testing, define warehouse models, and mentor teams while driving data governance and cross-functional alignment.

Design and maintain data pipelines, collaborate with stakeholders, ensure data quality, deploy infrastructure as code, and optimize reporting solutions.