Top Data Engineer Jobs in Portugal

Design and develop Big Data solutions using the Hadoop ecosystem (Pig, Hive, MapReduce, HBase, Spark, Kafka, Oozie). Implement Java-based services and scripting, tune performance for large datasets, participate in full project lifecycle, and collaborate with stakeholders. Role requires extensive travel and strong communication.

Develop and maintain Hadoop-based big data solutions using Pig, Hive, MapReduce, HDFS, HBase, Spark, Kafka and related tools. Implement web services/APIs, performance tune large datasets, script in Java and shells, participate in full project lifecycle, and collaborate with stakeholders. Extensive travel and on-site work required.

Develop and maintain Hadoop/Big Data solutions using Pig, MapReduce, Hive, HBase, Spark, Kafka and related tools. Build Java-based components, scripts, and APIs; tune performance on large datasets; participate in full project lifecycle and collaborate with stakeholders.

Develop ETL and data visualization solutions for a big data customer and partner experience transformation program. Build native Hadoop and Vertica ETL pipelines, collaborate with stakeholders, and support data integration and visualization deliverables for analytics teams.

Develop and maintain web applications and services using Microsoft .NET stack and JavaScript frameworks. Build solutions with AngularJS, NodeJS, MongoDB; design and implement WCF/SOAP web services, XML/XSLT; participate across SDLC from architecture through maintenance.

Design, build, and maintain data warehouses, data marts, and OLAP solutions; develop ETL frameworks, data models, SSIS/SSAS/SSRS artifacts, and performance-tuned SQL Server databases to support analytics and visualization projects.

Design, develop, and implement web applications using Microsoft .NET, AngularJS, Node.js and MongoDB. Build and consume web services (WSDL/SOAP/REST), XML/XSLT and WCF. Participate in all SDLC phases including architecture, development, testing, debugging, documentation and maintenance.

Design, develop, and maintain web applications using .NET, AngularJS, NodeJS and MongoDB. Build and consume WSDL/SOAP/WCF web services, work with XML/XSLT, participate in all SDLC phases including architecture, development, testing, debugging, documentation, and maintenance.

Design and develop Big Data solutions using Hadoop ecosystem (Pig, Hive, MapReduce, HBase, YARN, Spark, Impala, Kafka, Oozie). Implement Java and scripting-based pipelines, tune performance on large datasets, participate across SDLC phases, troubleshoot, and collaborate with stakeholders in a global delivery environment.

Provide technical leadership for the MFS data platform, collaborate with cross-functional engineering teams to improve platform adoption, mentor engineers, and design and develop large-scale distributed systems using Big Data technologies and Kafka.

Serve as the on-site operations representative for data center construction, enforcing QA/QC standards, conducting inspections and walkdowns, coordinating with design and commissioning teams, reviewing MEP designs for maintainability, documenting progress and test results, and resolving engineering issues to ensure construction meets operational requirements.

Lead data discovery and design of enterprise data models, contracts, validation rules, and QA practices. Define metadata, data quality checks, and governance; partner with stakeholders and engineers to implement automated validation and ensure reusable, trustworthy city data.

Design, develop, and enhance software products, focusing on scalable data platforms and requiring expertise in various programming and big data technologies.

Lead the design and implementation of data pipelines and infrastructure, set technical standards, mentor engineers, and manage data governance.

The Senior Data Engineer designs and maintains data pipelines and architectures to support enterprise analytics, ensuring data quality and integration across systems.

Design and build data pipelines and infrastructure; maintain data quality and accessibility; optimize data warehouse architecture while collaborating with teams to meet data needs.

Develop scalable data pipelines for autonomous driving, enhancing data extraction and labeling processes, and collaborating with cross-functional teams on ML models.

Lead software engineers at Pariveda design and deliver enterprise applications, guiding junior staff and solving complex client challenges using various technologies in an agile environment.

The Data Operations Engineer will monitor and optimize trading processes, enhance integration and deployment pipelines, ensure quality control, and lead continuous improvement initiatives while documenting and reporting on production performance.

The Staff Data Infrastructure Engineer will design and implement a scalable data platform, collaborating with engineering and product teams while ensuring data security and integrity.

Lead design and implementation of scalable real-time and batch data pipelines for programmatic advertising. Ingest, normalize, transform, and model bid stream, impression, and revenue data; maintain data warehouses and lakes; implement data quality and privacy compliance; support BI teams and optimize yield through analytics and feature engineering.

Lead presales for Data Analytics/AI solutions, secure technical closes, scope and design solutions with sales and delivery, hit productivity quotas, coach sales associates, manage partner relationships, and drive product growth and customer satisfaction.

As a Senior Staff Machine Learning Engineer, you will drive AI product development, collaborate with cross-functional teams, and enhance ML models at scale.

The Data Infrastructure Engineer will design, build, and maintain data tools for robot data management, supporting AI researchers and ensuring optimal data solutions across storage and compute resources.

Build and maintain scalable, reliable data pipelines and ingestion processes; support data integration, quality, governance, and cloud-based analytics platforms; troubleshoot partner/customer data environments; document standards and reference architectures; automate processes and ensure security and compliance for enterprise data solutions.