Top Data Engineer Jobs in Fortaleza

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.

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.

Design and maintain the graph data layer for a fashion advisory platform: schema design, batch ingestion, probabilistic entity matching and deduplication, editorial signal weighting, openCypher query optimization (sub-3s), and cross-functional collaboration with ML and GenAI agent developers.

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.

Design and build multi-modal data ingestion pipelines, manage embeddings in vector and graph databases, integrate AI-assisted agents, collaborate on evaluation pipelines, and deploy/optimize scalable data services and workloads on GCP using Python-based APIs and orchestration tools.

Lead the design and implementation of robust data pipelines and ecosystems. Ensure scalability and reliability while mentoring team members in best practices.

Build and evolve a SaaS platform for lab testing and regulatory compliance. Responsibilities include full-stack development, systems architecture, technical trade-off evaluation, security implementation, and close collaboration with founders and customers during early-stage product development.

Develop and support enterprise Salesforce platforms through custom CRM development or Salesforce Data Cloud implementation. Responsibilities include building Apex, Lightning Web Components, Flows, integrations, automation, AI-enabled capabilities, data models, identity resolution, harmonization, SQL transformations, and scalable activation solutions. The role also involves security governance, performance optimization, troubleshooting, code reviews, release management, technical documentation, and collaboration with US-based teams during Central Time hours.

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.

Lead a data engineering squad delivering strategic, low-latency data products on Azure. Responsibilities include designing data architectures, building Python REST APIs and Databricks/dbt pipelines, implementing Terraform infrastructure, ensuring data quality and observability, driving CI/CD, resolving production issues, and mentoring engineers. The role requires hands-on technical leadership, advanced data modeling and SQL expertise, and collaboration with business and technical stakeholders.

Build and own end-to-end analytics data infrastructure: design and maintain ETL/ELT pipelines, model data in dbt, govern semantic layer and metrics, implement testing and observability, partner with stakeholders for requirements, run analyses and A/B test evaluation, and leverage AI tools to accelerate engineering and analytics workflows.

Lead end-to-end development of data products: ingest, model, transform, validate, and deliver datasets for analytics and AI. Translate business requirements, ensure data quality, build scalable pipelines (SQL/Python/BigQuery), enable AI consumption, and communicate solutions to stakeholders while driving proactive improvements.

The Senior Data Developer extends and maintains existing code repositories, builds ETL/ELT pipelines, integrates enterprise data from BigQuery and SAP, and designs database structures. Depending on expertise, the role also supports front-end and backend application development, scheduled jobs, and machine learning for computer vision and forecasting. The developer follows Git best practices, communicates clearly, and collaborates with distributed cross-functional teams across supply chain, manufacturing, logistics, and procurement initiatives.

Senior AWS Data Engineer to design and operate AWS-based data solutions and Snowflake-centric platforms. Build data pipelines, implement Snowflake AI components, design data marts/warehouses/lakes, produce architecture drafts, and collaborate with architects and stakeholders for handoff to operations.

Build and operate Kubernetes-based data security services across public cloud, private cloud, and on-premises environments. Develop deployment, infrastructure, configuration, observability, and operational automation; troubleshoot complex production issues across applications, networking, and cloud platforms; improve scalability and reliability; support on-call operations; and contribute to technical initiatives, tooling, runbooks, and engineering best practices.

Lead design and delivery of Databricks-based Lakehouse solutions and end-to-end data engineering projects. Build and optimize ETL, streaming and batch pipelines, integrate cloud and on-prem systems, mentor team members, support pre-sales, and ensure governance, security, and platform performance.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.

Own the reliability, release, and customer-facing correctness of core data products. Manage end-to-end product releases, build proactive data quality monitoring, perform root-cause analyses, respond to data incidents, coordinate fixes with Engineering and Data Science, run QA tests and Looker/BI releases, investigate discrepancies versus customer first-party data, and partner with Commercial to design customer-facing data solutions.

Lead development and maintenance of data services and pipelines, design efficient data models, improve architecture and monitoring, build ingestion/processing and provisioning processes, ensure data quality/security, design APIs, and collaborate with business teams to create data products.

Own technical delivery and reliability of cloud-native data platform environments on AWS/Azure for managed clients. Implement and maintain IaC and CI/CD, monitor and resolve production pipeline issues, collaborate with architects and stakeholders, produce runbooks and documentation, mentor peers, and contribute reusable IaC and automation for Elastic Platform Operations.

Backend-focused full-stack engineer building scalable, data-driven applications, APIs, integrations, and data pipelines. Work includes cloud-hosted services, containerized deployments, frontend work with modern frameworks, and contributing to AI-powered features, architecture, and cross-team delivery.

Build and operate shared AI platform infrastructure for LLM applications, agents, RAG, tool calling, and AI workflows. Develop data and knowledge pipelines, secure enterprise integrations, reusable frameworks, deployment and observability standards, and developer tooling. Take AI systems from experimentation to reliable production while ensuring security, privacy, governance, scalability, quality, and cost efficiency. Collaborate across Engineering, Product, and IT to establish practical AI platform patterns and guardrails.

Build, deploy, and maintain production-grade ML models and data pipelines in cloud environments. Perform EDA and feature engineering, monitor model performance and drift, and collaborate with engineering and business stakeholders. Contribute to AI/LLM initiatives and ensure solutions operate reliably in production.

Design, build, and maintain reliable, scalable ELT/ETL data pipelines and expand the data warehouse. Partner with technical and non-technical stakeholders to deliver analytics-ready data, support reporting, and work with cloud-based data stack technologies.

As a Senior Data Engineer at Zencore, you will leverage Google Cloud to assist clients in data transformation and application modernization, covering ETL/ELT, data warehousing, and streaming data systems.