Top Data Engineer Jobs in Portugal

The Data Engineer will develop and implement software solutions for law enforcement, focusing on data management, system optimization, and effective communication with clients.

The Data Engineer will develop data and software solutions for law enforcement, create reports, and manage data sources. Responsibilities include collaborating with teams to analyze data, communicate with clients, and ensure high-performance analytics tools.

Design and maintain scalable data pipelines, ensuring accuracy and reliability. Collaborate with engineers to optimize data systems and enforce best practices.

Design and maintain scalable batch and real-time data pipelines, build governed data models for a CDP, ensure data quality and security, support identity resolution and customer 360, mentor junior engineers, and collaborate with analytics and business stakeholders to deliver actionable customer insights.

Build, maintain, and support scalable data systems and pipelines. Ingest, clean, structure, and enrich data from multiple sources. Ensure data quality, security, and governance. Collaborate with analysts and data scientists to design solutions, deploy and optimize pipelines, and diagnose and resolve data issues to support analytics and reporting.

The role involves engineering modern data platforms, focusing on data ingestion, transformation, and storage while utilizing Azure services and MS Fabric, supporting an investment management firm.

As a Software & Data Engineer, you will design data pipelines, deploy AI tools, build data warehouses, and integrate systems for clients, ensuring data quality and performance.

Design, build, and maintain cloud-based, enterprise data pipelines and automation to support cybersecurity detection, reporting, and analytics. Implement ETL/ELT processes, ensure data quality and governance, integrate security tools/APIs, develop SOAR playbooks and custom automation (Python/PowerShell), and support CI/CD deployments, dashboards, and monitoring while mentoring junior staff.

Design and deliver high-value human data and evaluation projects for LLMs. Partner with model teams, build tooling to measure data effectiveness, maintain data integrity, design annotation workflows, run evaluations, and scale Human Data operations to improve model quality and truthfulness.

Owns the reliability, operability, security integration, and cost efficiency of a cloud data platform centered on Snowflake. Responsibilities include Snowflake administration, query optimization, cost attribution, orchestration, CI/CD, infrastructure as code, monitoring, incident response, access controls, migrations, disaster recovery, compliance evidence, and engineering enablement. The role is hybrid with at least 40% onsite work and operates Monday through Friday during standard business hours.

Build and operate real-time, event-driven microservices that process large-scale penetration-testing telemetry into findings, attack paths, reports, and customer insights. Responsibilities include migrating batch logic to streaming architectures, designing distributed systems, managing schemas and storage, correlating hosts and identities, ensuring observability and operational excellence, and contributing to technical design, APIs, documentation, and incident response.

Lead the design and development of sales data models, CRM-integrated AI agents, and permissioned write paths for revenue teams. Build scalable GenAI interfaces for complex workflows with transparency and human approval. Partner with product, design, and engineering teams to define initial use cases, launch MVPs, and drive adoption. Provide technical and people leadership in a 0-to-1 product environment.

Lead design and delivery of large-scale, cloud-native services powering millions of devices. Architect solutions for scalability and integration, own full service lifecycle in Kubernetes, mentor engineers, participate in on-call support, and drive engineering best practices across cross-functional teams.

Designs, deploys, and optimizes enterprise data protection across Microsoft 365, endpoints, cloud applications, SaaS platforms, and network egress points. Manages DLP, data classification, Microsoft Defender, Sentinel, Varonis, and DSPM capabilities; investigates incidents, reduces false positives, assesses data exposure, and drives remediation. Develops data handling standards, automation, documentation, reporting, and security metrics while partnering with IT, Legal, Compliance, and business data owners.

Design, build, and maintain scalable ETL/ELT pipelines (batch and streaming); manage data catalogs and API integrations with OAuth2; collaborate with cross-functional teams to ensure data quality and platform reliability for mission-driven projects.

Design and build scalable backend services and data ingestion pipelines, perform data modeling and large-file processing, develop reusable APIs, and optimize systems for speed and scalability while collaborating in an Agile team.

Design and maintain scalable data models and version-controlled analytics workflows to convert raw data into trusted datasets for BI. Collaborate with data engineering and business teams to ensure data quality, documentation, and alignment with business objectives, enabling reliable reporting and analytics.

Design, build, and maintain core data infrastructure and automated end-to-end pipelines to ingest, process, store, and transform data. Develop data models and schemas, implement processing frameworks, enforce data quality and lineage, troubleshoot pipelines, collaborate with data scientists and security teams, document architecture, and support data governance and continuous improvement.

Design, configure, and operate data governance tooling for unstructured and structured data. Implement metadata cataloging, lineage harvesting, automated data quality checks, and governance workflows. Build prototypes, dashboards, and connectors using Python, SQL, Databricks, and Streamlit. Collaborate with stakeholders to define critical data elements, prioritize remediation, produce documentation, and drive tool adoption via trainings and enablement.

Intern will support enterprise data and advanced analytics initiatives, including AI solution development, data discovery, data extraction and pipeline development, data modeling, insight generation, and visualization. The role involves combining data from business systems, IoT, telematics, and digital initiatives, while collaborating with IT, cross-functional teams, mentors, and leadership. The hybrid internship is based in Columbus, Indiana, with remote work Thursdays and Fridays.

Design, build, and own business-critical data infrastructure, including AWS Glue and Spark pipelines, cloud warehouse or lakehouse systems, orchestration, and Tableau BI platforms. Define event-driven contracts, data models, schemas, observability, and batch or streaming architectures across multi-tenant, multi-region environments. Collaborate across producer and consumer teams while owning production data infrastructure and outcomes end to end.

Design and support ETL, data migration, transformation, import, export, and validation workflows for moving legacy EHR data into athenahealth systems. Build Python and SQL utilities, define source-to-target mappings, support HL7 and FHIR interoperability, validate EHI exports, investigate data quality issues, document processes, and collaborate with product, implementation, operations, sales, and engineering teams in a remote environment.

Design, develop, and optimize data warehousing and ETL/ELT solutions on Microsoft SQL Server and cloud platforms. Perform data profiling, cleansing, dimensional modeling (Kimball), and performance tuning. Build semantic models and Power BI/Microsoft Fabric solutions, advise on architecture, mentor teammates, assist in pre-sales/scoping, and promote BI best practices across clients.

Design and build large-scale ETL/ELT pipelines and analytical architectures on GCP. Assess, plan, and drive enterprise data and cloud migration programs, modernize legacy systems, optimize query performance, and enable scalable cloud-native solutions for clients.

Build and operate full-stack interfaces, backend services, APIs, data pipelines, dashboards, and monitoring for human feedback and RL data collection. Partner with RL researchers to translate data needs into reliable collection campaigns and training-ready datasets. Own projects end to end, improve system latency and usability, and identify bottlenecks between data collection and model training.