Top Data Engineer Jobs in Portugal

As a Data Engineer, you will create and optimize datasets and data pipelines, focusing on large-scale data collection across various modalities and collaborating with multiple teams.

The AI-Native Data Engineer will build data pipelines, architect databases, design GCP infrastructure, and automate workflows for energy management using AI.

Develop and design data pipelines, optimize data delivery and automate processes to support end-to-end solutions in various settings.

As an AWS Data Engineer, you will develop data pipelines, create data models, troubleshoot issues, and collaborate with teams while utilizing tools like PySpark and SQL.

Design and own the companys end-to-end data and analytics stack: build metrics, dashboards, and reporting; instrument product and GTM signals; transform messy data into structured inputs for AI agents; and define analytics infrastructure (dbt, Snowflake, Mode) while partnering across founders, engineering, and GTM teams.

Solutions Engineers at Vori configure grocery platforms for retailers, handling data integration and customer interactions to solve complex problems.

The role involves structuring and ingesting customer data, setting up ETL pipelines, and providing solutions for data export and import.

The Principal Data Engineer will lead data architecture initiatives, design scalable data systems, and oversee data standards across teams while ensuring high-quality data infrastructure and guiding engineering teams.

The Gen AI Data Engineer will design and build distributed data systems, develop data pipelines, manage data infrastructure, and integrate technologies for real-time and batch processing, contributing to scalable analytics solutions.

Lead implementation of ETL patterns and reliable data infrastructure, partner with business and engineering teams to prioritize and deliver integrations, APIs, and reusable components, and support operational data applications in an Agile environment.

The role involves developing software solutions in collaboration with cross-functional teams, utilizing cloud technologies and agile methodologies, while optimizing processes and data analytics.

As a Business Intelligence/Data Engineer, you will analyze data structures, implement data warehousing solutions, and design data visualizations while working with AI/ML services in cloud environments.

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.

Leads the design, implementation, and optimization of cloud-native data infrastructure, platforms, storage solutions, and complex data pipelines. Establishes engineering standards for testing, monitoring, releases, and reliability; troubleshoots cross-system failures and leads incident reviews. Partners with infrastructure, laboratory IT, data science, bioinformatics, and scientific teams to deliver secure, scalable solutions. Mentors engineers, guides technical initiatives, and uses generative AI to accelerate development and data transformation.

Build and maintain marketing data pipelines, replace spreadsheet workflows with structured databases, automate data cleaning/deduplication and syncing, develop AI-driven reports and dashboards, and collaborate with marketing, AdOps, and engineering to ensure reliable, unified, AI-ready marketing data.

Design and implement data ingestion, transformation, and Foundry applications using Palantir Foundry AIP. Develop workflows in Workshop and Ontology Manager, integrate AI/ML models, support analytics and mission-focused solutions, collaborate with stakeholders, follow Agile and DevSecOps practices, and manage Git/CI/CD pipelines. Role requires eligibility for Secret clearance and a bachelor's degree.

Design, build, and maintain secure, scalable data pipelines and ML data workflows to support IRS fraud detection and compliance analytics. Collaborate with data scientists and stakeholders, troubleshoot production issues, produce documentation and briefings, and operate in restricted government environments.

The Senior Data Engineer will design and build data systems, mentor engineers, and collaborate with product teams to optimize data architecture and pipelines for scalability and reliability.

The Data & AI Engineer III will lead cloud data engineering solutions, develop data infrastructure, establish architectures, mentor team members, and secure data around cloud systems.

The Staff Business Systems Engineer will manage systems supporting Data Center operations, focusing on capacity planning, lifecycle management, integrations, and asset tracking across infrastructure and finance. Responsibilities include architecture design, data modeling, and ensuring operational compliance.

Design and operate backend systems for high-volume, multi-stage data pipelines that de-identify sensitive enterprise data. Own versioned contracts, lineage, state transitions, retries, replay, reconciliation, verification, migrations, and safe recovery. Diagnose production behavior across services and storage, improve correctness and throughput, and build auditable release controls. Collaborate with ML, data, platform, security, product, and customer-facing teams while using AI tools with deterministic safeguards and human review.

Develops and deploys predictive analytics, machine learning, and deep learning solutions for production use. Responsibilities include statistical analysis, feature engineering, model development, anomaly detection, NLP, computer vision, MLOps, CI/CD pipelines, cloud AI platform integration, data engineering collaboration, model monitoring, governance, and stakeholder communication. The role also supports large-scale analytics, AI modernization, and human-reviewed multi-source summarization.

Design, build, and operate scalable batch, streaming, and CDC data pipelines across Snowflake and AWS. Lead ETL/ELT architecture, orchestration, data modeling, quality monitoring, observability, incident resolution, and performance optimization. Develop production solutions in SQL and Python, establish engineering standards for testing and CI/CD, and partner with Analytics Engineering, Data Science, Product, and Engineering teams to deliver reliable data products.

Lead design and evolution of FloQast's core data platform and lakehouse, owning event streaming, batch and streaming ETL, storage, governance, and compute architecture (Spark). Define standards, mentor engineers, collaborate cross-functionally, and ensure scalable, secure, production-grade data infrastructure for product and analytics.

Build scalable data solutions and reconciliations to automate Compliance & Finance regulatory processes. Partner with stakeholders to translate requirements into designs, lead technical development, review code, and evolve standards for data pipelines and reporting.