Top Data Engineer Jobs in Portugal

Design, build, and productionize end-to-end AI and generative systems that convert unstructured content into structured knowledge. Develop retrieval, semantic search, and knowledge-driven pipelines; ensure evaluation, reliability, explainability, and ongoing monitoring. Collaborate cross-functionally to align AI solutions with business priorities and optimize for latency, cost, and throughput.

Data Engineers at Rhombus Power design and implement data infrastructure, analyze and maintain data systems, and collaborate across teams. They develop code for data processes and ensure strong client connections.

Design, build, and maintain scalable data pipelines using Fivetran and Snowflake. Develop and optimize dbt models, ensure data quality and governance, implement data modeling best practices, troubleshoot performance, and collaborate with analysts and business teams to improve data accessibility and engineering practices.

This role offers training for aspiring Data Engineers, focusing on technology of choice, interview preparation, and skill enhancement with guidance on OPT STEM extension.

The Data Engineer II will design and maintain ELT pipelines to support analytics across Sony Pictures. This role involves collaborating with stakeholders, delivering data models, ensuring data integrity, and driving agile project delivery within a team environment.

The Data Engineer will build and manage scalable data pipelines and databases, collaborate on architecture, and drive data systems towards real-time capabilities in a fast-paced, innovative environment.

Design, build, and maintain scalable data processing pipelines and infrastructure (ETL/ELT). Implement large-scale data processing (Spark, MapReduce, Kafka), support stakeholders with analysis and reporting, ensure secure multi-region data handling, and optimize data delivery for analytics and business insights.

Develop, publish, and maintain digital technical documentation for mail processing equipment; research OEM/engineering sources; draft maintenance and assembly procedures; troubleshoot equipment workflows and perform on-site or desktop validation; identify documentation noncompliance and provide corrective correspondence; support team deliverables and travel up to 25% to collect technical information.

Implement and maintain data pipelines and platforms: data acquisition, preparation, ETL, and database architecture. Build, containerize, and deploy automated data and ML pipelines in cloud environments (AWS). Work in Agile teams with developers and data consumers, apply MLOps concepts, and support real-time/streaming data solutions. Top Secret clearance and a bachelor’s degree required.

Lead architecture, build, and maintain scalable people-data platforms and pipelines. Establish data quality frameworks, governance, lineage, and data contracts. Drive consistent modeling, monitoring, and schema evolution. Lead cross-functional initiatives, mentor engineers, and collaborate with analytics, data science, and HR to enable recruiting, workforce planning, and employee lifecycle insights. Advocate emerging data technologies and integrate AI to improve workflows and impact.

Architect and build high-scale product telemetry and lakehouse data solutions. Lead cross-functional initiatives, implement data contracts, enforce data quality, lineage, governance, and mentor senior engineers to drive reliable, scalable product analytics.

Build and operate real-time and batch data pipelines for maritime domain awareness. Parse binary sensor and SONAR data, design high-performance data warehouses, validate and normalize sources, and improve local development and CI/CD. Work on-site with customers, write production-quality code, and integrate systems to support mission-critical deployments.

Build and maintain scalable data pipelines and integrations using Databricks and Azure. Transform and model large structured and unstructured datasets to support reporting, analytics, and operational needs. Implement data quality, monitoring, and governance; troubleshoot pipeline performance; collaborate with stakeholders and mentor junior engineers.

Design, build, and maintain ETL pipelines and big-data workflows from ingestion to consumption. Troubleshoot data/system issues, work in an agile team, handle structured/unstructured data and security logs, and support government data acquisition and sharing while meeting quality assurance and testing standards.

Build and maintain R&D data infrastructure for experimental, manufacturing, and QC data. Implement DAQ integrations, databases, validation checks, analysis pipelines, visualizations/dashboards, and perform statistical/regression analyses to support cross-functional technical teams.

Designs, develops, and maintains data pipelines and computational workflows for large scientific datasets. Implements ETL and workflow automation, performs data validation and provenance documentation, optimizes high-volume processing, and collaborates with technical and domain teams to troubleshoot and tailor solutions for analysis-ready, reproducible data.

The candidate will improve the data ETL pipeline, build analytical tools, manage operations, and expand into new markets and asset classes.

Support development and integration of AI and cloud solutions by extracting, transforming, and standardizing text, JSON, and geospatial data for ML ingest. Build and automate data pipelines, assist ML integration and testing, coordinate data requirements with stakeholders, and work in a multidisciplinary team under security-sensitive deadlines.

The Data Engineer will design and manage data pipelines, develop datasets for product metrics, and collaborate with teams to ensure robust data integration and processing.

Design and build scalable platforms for data ingestion and storage, collaborate with hardware engineers, and define engineering processes at Sift.

Embed with enterprise clients to discover, build, and deploy AI agent workflows against messy, unstructured data. Lead technical onboarding, construct connectors and pipelines, run production deployments, serve as primary technical contact, feed product insights back to engineering, and create repeatable implementation playbooks.

Build and maintain production data pipelines and models to support analytics, ML, and research. Ensure data quality and availability, operate and monitor the data platform, investigate failures, optimize performance and costs, and collaborate with Product, Engineering, and Research teams.

The Lead Data Engineer will design and build data architectures, lead a team, and develop ETL processes using cloud technologies to optimize data solutions for the U.S. business.

This role involves defining data reliability strategies, establishing company-wide standards for data quality, and leading cross-functional data engineering initiatives at Circle.

As a Software Engineer in Data, you will enhance core product experience, optimize integrations, manage data systems, and support scalable services.