Top Data Engineer Jobs in Portugal

Design, build, and maintain end-to-end data pipelines and ETL for enterprise data warehousing using Azure Data Factory, DBT, and Snowflake. Optimize SQL, support integrations and batch ingestion, mentor team members, document data interfaces, improve data quality and monitoring, and collaborate with stakeholders to translate business needs into scalable data solutions.

Design, build, test, and maintain scalable ELT data pipelines and architectures using Python and Databricks. Implement data security and entitlements, update data models, perform statistical analysis, use AWS services, and apply/validate enterprise-authorized AI-assisted tools to accelerate development while ensuring traceability, auditability, and data sensitivity controls.

Develop, deploy, and operate production-grade Nextflow pipelines and cloud-based omics data platforms. Integrate and harmonize complex biological and clinical datasets (single-cell, bulk transcriptomics, ATAC-seq, proteomics, genetics) for ML model training and inference. Implement DevOps and CI/CD practices, manage pipeline lifecycle, collaborate with scientists and external partners, and deliver reusable, scalable data products to support AI-driven discovery.

Build, test, monitor, validate, and support scalable ETL/ELT data pipelines and validation frameworks in Azure/Databricks. Perform source-to-target validation, data profiling, reconciliation, anomaly detection, automation, and production support to ensure data quality and reliability for analytics and reporting.

The Principal Data Engineer will lead data engineering efforts on public cloud, design scalable solutions, build data pipelines, and drive deliverables with a high level of technical expertise in relevant technologies.

The Market Data Engineer will design and implement high-performance market data systems, focusing on trading infrastructure, performance optimization, and system resiliency.

The Solutions Data Engineer will manage data integrations end-to-end, utilizing FloQast's Data Platform and Data Studio to connect source systems and transform data for reliable outputs across applications.

Design, build, and maintain scalable ETL/data pipelines for Comcast's national locates program. Ingest and transform data from multiple internal and external sources, optimize performance and cost, ensure data quality and governance, collaborate with analysts and stakeholders, troubleshoot pipelines, and document data structures and processes.

Design, build, and maintain scalable batch and real-time data pipelines on an Azure data platform using Azure Data Factory, Databricks, and PySpark. Implement Medallion architecture (Bronze/Silver/Gold), enforce data quality and governance, optimize Spark jobs and cluster usage, monitor pipeline health, and collaborate with data science and reporting teams. Document lineage, handle CDC/incremental loads, and integrate streaming ingestion and validation checks to ensure reliable analytics and ML-ready datasets.

Design, build, and maintain data architectures and pipelines to ensure data quality, lineage, and accessibility. Implement ingestion frameworks and transformations across cloud and on-prem platforms, enable data consumption (APIs, views, extracts), monitor data observability, and collaborate cross-functionally to meet privacy and business requirements. Work may include nights/weekends and requires independent technical judgment.

As a Data Engineer at OneImaging, you'll design and maintain scalable data pipelines and infrastructure, collaborate on analytics, and ensure compliance with healthcare regulations.

As a Senior Associate Data Engineer, you will build and maintain data pipelines, support analytics initiatives, and uphold data standards while collaborating with teams on data strategy and governance.

Develop and maintain data cleaning, preprocessing, integration, and quality checks. Build dashboards and complex visualizations (Tableau or Power BI). Apply AI/ML techniques for unstructured data analysis, provide data-driven analysis, and perform quality assurance to support government/intelligence decision-making.

Lead design, implementation, and maintenance of production-scale AWS data services and pipelines (batch & streaming). Drive data ingestion, modeling, warehousing, lakes, APIs, and governance; coordinate S3, SQS, Lambda, DynamoDB; collaborate with product and analytics teams; ensure reliability, scalability, and compliance in defense-related analytics solutions.

Design, build, and maintain large-scale data pipelines and lake architectures on AWS using Spark/Databricks. Ensure data quality, lineage, observability, and TDD-based testing. Support self-service analytics, semantic layers, and cross-functional stakeholders while providing production support and documentation.

Design, build, and operate big-data and streaming platforms (Kafka, Hadoop, Dremio). Develop, deploy, and monitor data pipelines using Java, Python, Spark/Flink; collaborate on data modeling and ingestion, ensure data integrity and availability, act as a Big Data SME, and perform root-cause analysis.

Build, operate, and scale large-scale data storage platforms (Hadoop, S3-compatible, enterprise storage). Tune Linux for I/O performance, automate deployments with Ansible/Puppet, implement observability with Prometheus, and partner with engineers on storage- and Kafka-focused data solutions. Familiarity with Kubernetes and development in Java/Python required.

Entry-level Junior Data Engineer to help build and maintain cloud data pipelines into BigQuery using tools like Fivetran and Python. Contribute to Medallion (bronze/silver/gold) layers, data transformations (dbt), basic data quality checks, CI/CD practices, documentation, and governance while learning cloud engineering, orchestration, and observability under senior engineers.

Design and build data models, data warehouses/lakes, and automated ETL pipelines; integrate diverse data streams; structure unstructured data; develop data collection frameworks, standards, governance, and analytic tools; manage and troubleshoot data feeds in complex, multi-enclave environments to support cyber and operational intelligence products.

Design, build, and maintain scalable data systems and pipelines (batch and streaming). Implement data formats, modeling, and infrastructure; support deployment automation, stakeholder requirements, and data governance to enable analytics and reporting.

Develop and maintain Python data pipelines and CI/CD deployments to build business data objects. Implement source-to-target mappings, unit and integration tests, resolve complex technical issues, and uphold software quality and best practices within the team.

Design, implement, and operate geospatial data architectures and automated processes to support GEOINT analysis. Develop and integrate algorithms, tools, and models for SAR, EO, MTI, LiDAR, OPIR and related data; perform data integration, governance, metadata management, and analytics to produce intelligence products and briefings.

Design, build, and maintain scalable data architecture and pipelines to support medical analytics and predictive models. Partner with product and data leads, prepare datasets, automate data delivery, mentor colleagues on standards, and evaluate tools to improve analytics productivity and stability.

Design, build, and maintain a Microsoft Fabric Lakehouse and Azure Synapse data warehouse. Develop and optimize ETL/ELT pipelines, dimensional/semantic models, and data quality checks. Tune SQL and Spark workloads, implement monitoring/alerting, handle production incidents, and integrate source systems and APIs. Partner with stakeholders, follow Agile practices, document runbooks, and participate in code reviews.

Lead design, implementation, and operationalization of scalable analytics pipelines and BI solutions on cloud platforms (Databricks). Drive data quality, lineage, security, governance, performance tuning, and AI-enabled insights. Partner with product, engineering, and business stakeholders and mentor analytics engineers across global delivery models.