Top Data Engineer Jobs in Portugal

Serve as functional DBA for Oracle and MongoDB across environments. Partner with app teams on schema design, data modeling, and query optimization. Diagnose and resolve database performance, integrity, and incident issues. Validate database changes for releases, maintain documentation/runbooks, and support data migration and transformation for enterprise-scale applications.

Design, build, and lead scalable data platforms, pipelines, data products, and ELT/ETL processes supporting analytics and AI/ML workloads. Establish standards for data quality, testing, monitoring, observability, CI/CD, security, and reliability. Own projects through deployment and production support, collaborate across technical and business teams, use AI-assisted engineering tools, and mentor data engineers.

Lead the architecture and development of scalable AWS-based data ingestion, transformation, and orchestration pipelines. Build reliable data infrastructure using Python, SQL, Airflow, Lambda, ECS, SQS, and Terraform. Establish data modeling, quality, lineage, monitoring, testing, and observability practices while partnering with analysts, scientists, and backend engineers. Mentor senior engineers, guide technical decisions, and provide hands-on leadership for complex data platform initiatives.

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

Design, build, and own scalable data pipelines and evaluation systems that power production AI features and internal analytics. Ensure data quality across ingestion, modeling, and reporting, collaborate with ML and analytics teams, deploy and monitor ML systems, and establish standards for data work and evaluation.

Lead technical strategy and architectural evolution in marketing technology, mentoring teams and driving innovative solutions across data engineering and platforms.

Partner with clients and internal teams to design and implement technology solutions. Lead modules and teams, translate business problems into technical designs, support delivery activities, and guide solution artifacts from requirements to deployment.

Designs, builds, and maintains scalable data systems, streaming and batch pipelines, data infrastructure, data models, and deployment pipelines. Develops data products using cloud-based technologies, optimizes existing architectures, improves data formats and accessibility, and collaborates with analytics teams and stakeholders to deliver reliable solutions for reporting and decision-making.

Build, test, deploy, and support reliable data pipelines serving client-facing websites and reports. Investigate data discrepancies, optimize data loading, improve data onboarding and integrity, and collaborate with global teams and business stakeholders. Participate in Agile delivery, release management, technical research, and proof-of-concept work. Provide reporting and data insights to senior partners.

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.

The Data Engineer II will build and optimize data pipelines, develop reusable data sets, and ensure data integrity and security.

Design, implement, and manage advanced data ecosystems and scalable ETL/ELT pipelines. Analyze complex problems, deliver data warehousing and governance solutions, mentor junior staff, and build client relationships while maintaining high standards and business-context awareness.

Lead design and implementation of enterprise data architecture and cloud-based data solutions. Manage large data engineering projects, develop data models and pipelines, ensure governance and security compliance, advise clients strategically, and build high-performing inclusive teams.

The Data Engineer will design and develop Java-based applications, optimize APIs, and collaborate with teams, focusing on cloud integration and SQL queries.

As a Data Engineer at Whatnot, you'll design data architectures, implement data workflows, ensure data quality, and support analytics across teams.

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.

Builds and maintains scalable data systems, streaming and batch pipelines, data infrastructure, data models, and deployment pipelines. Develops data products using cloud technologies, improves data architectures, implements data formats and engineering frameworks, and partners with analytics teams to gather requirements. Requires programming and SQL expertise, Snowflake experience, and familiarity with DevOps, Terraform, and Docker.

Design and operate scalable batch and streaming data platforms supporting machine learning and generative AI. Build pipelines for structured, unstructured, OCR, document, and image data; develop RAG, semantic search, and LLM-powered solutions; and establish data quality, observability, governance, orchestration, and deployment practices. Partner with stakeholders, lead platform scalability and cost optimization, mentor engineers, and translate ambiguous needs into production-ready technical roadmaps while securely handling sensitive data.

The Data Engineer supports the data and analytics lifecycle by designing, developing, testing, and maintaining Big Data applications and pipelines. Responsibilities include building Spark and Scala processing logic, optimizing Hive and SQL queries, and maintaining integrations with HBase, Solr, JanusGraph, Kafka, and downstream systems. The role collaborates across business and technology teams, supports solution design and estimation, and develops skills through mentorship.

Design, build, and maintain scalable Spark-based ETL pipelines and computed tables in a central data lake. Integrate structured and unstructured IoT, sensor, and external data for analytics, model training, and dashboards. Collaborate with Data Science, Analytics, and ML teams to ensure reliable, high-quality customer-facing datasets.

Develop and maintain data pipelines and BI solutions for smart factory initiatives. Design data models, document data warehouses, build KPI dashboards, support ad-hoc analysis, and collaborate with stakeholders to enable data-driven decisions.

Configure and maintain Adobe Experience Platform and Real-Time CDP data pipelines, XDM schemas, datasets, identity resolution, Profile enablement, segmentation readiness, and destination activation. Validate data quality, troubleshoot ingestion and identity issues, manage sandboxes, and implement privacy, consent, governance, and access controls. Partner with architects, data engineers, consultants, analysts, data scientists, and marketing teams to support reporting, personalization, and machine learning readiness.

Build end-to-end data engineering and data science solutions for robotics and manufacturing data. Responsibilities include designing scalable multimodal data pipelines, creating analytic datasets, applying statistical and machine learning methods, developing MLOps workflows, building APIs and internal tools, and improving data governance, reliability, automation, and production support. The role partners cross-functionally with robotics, manufacturing, software, and data teams.

Design, develop, test, and maintain scalable data pipelines and architectures. Ensure data quality, lineage, governance, and security. Build automated testing/monitoring, optimize storage and query performance, update data models, and support data integration using SQL/NoSQL, cloud, and distributed processing frameworks.

Design, develop, test, and maintain out-of-the-box data connectors and parsers to ingest security logs into CrowdStrike Next‑Gen SIEM. Build lab environments, troubleshoot ingestion issues, collaborate on logging and normalization best practices, produce technical documentation, provide on‑call support, and work with customers and internal teams to resolve data ingestion incidents.