Top Data Engineer Jobs in Portugal

Lead design and implementation of cloud-based data pipelines and architectures. Advise clients on best practices, build/operate distributed ingestion and processing systems, mentor engineers, and collaborate with stakeholders to deliver scalable data warehousing and lake solutions using modern cloud technologies.

Serve as a trusted advisor designing, implementing, and maintaining cloud data pipelines and systems. Partner with product owners and SMEs to translate requirements, provide technical leadership, mentor engineers, and ensure scalable, supportable data solutions using modern cloud and ETL technologies.

Design, implement, and maintain cloud-based data pipelines and warehousing/lake solutions. Advise clients on best practices, lead technical decisions, mentor engineers, and collaborate with stakeholders to deliver scalable, maintainable data engineering solutions.

Design, implement, and maintain cloud-based data pipelines and platforms. Advise clients on best practices, integrate disparate data sources, optimize data warehousing/lake solutions, mentor engineers, and ensure scalable, maintainable data architectures across cloud environments.

Support design and implementation of data visualization, reporting, and ETL solutions in an Azure data lake/warehouse environment. Build data pipelines, develop scalable data assets, design logical and physical data models, retrieve and analyze data with SQL, and create Power BI/Tableau/Qlik visualizations. Participate in Agile delivery, data profiling, and data governance while collaborating with business stakeholders to translate requirements into analytics solutions.

Implement, automate, secure, and operate cloud and data platform capabilities for mission-oriented environments. Responsibilities include infrastructure-as-code, CI/CD, Linux administration, ETL and data pipeline development, API integration, testing, and ensuring resilient, mission-ready deployments with DevSecOps practices.

Lead design, validation, and optimization of utility-scale BESS for data centers and grid applications. Responsibilities include battery sizing, lifecycle modeling, vendor model review, inverter/PCS and controls validation, EMS/Plant Controller and SCADA integration, and operational analytics using Python and engineering tools.

Design, build, and maintain scalable data pipelines and ingestion agents for fleet robotics. Define efficient data formats/protocols, implement observability and big-data processing (Spark/Kafka), and build cloud tools (S3, Athena, SageMaker) for analytics, IoT integration, and long-horizon forecasting to improve robot performance and operational insights.

Provide mechanical engineering support across data center site selection, design review, construction, commissioning, turnover, and enduring operations. Ensure mechanical systems meet redundancy, maintainability, and reliability standards; lead troubleshooting, root cause analysis, preventative maintenance, asset lifecycle planning, water treatment programs, and standards development to improve uptime and operational readiness.

The Data Science Engineer will develop and apply machine learning and data science algorithms for cybersecurity and power systems, designing solutions and collaborating with multidisciplinary teams to enhance critical infrastructure resilience.

Lead development and integration of instrument drivers for major Chromatography Data Systems. Build WPF desktop UIs and Node.js backend tools, create installers with Wix, use VMware for testing, collaborate with hardware engineers, and ensure robust communication between HPLC instruments and third-party CDS platforms.

Lead architecture and technical direction for data integrations moving clinical and financial data at scale. Design orchestration, pipelines, data stores, observability, security/compliance, and mentor engineers while owning end-to-end cross-system initiatives and reliability.

Designs, builds, and deploys data pipelines and integrations to support analytics and machine learning for digital pathology. Partners with product owners and ML teams to extract, transform, validate, and visualize data, enabling insights and model development. Maintains platform knowledge and provides consultative support across teams.

Lead teams to design, build, and operate scalable, AI-ready data pipelines and ecommerce data products (especially Shopify). Build semantic layers, curated data marts, and data models; ensure data quality, monitoring, and performance. Collaborate with data scientists, analysts, and governance to support feature engineering, ML datasets, and reliable downstream consumption.

Develop and implement ServiceNow SecOps and IRM solutions, build and validate data ingestion pipelines, create automation with Flow Designer, script server/client logic, maintain REST integrations, schedule background jobs, and debug/modernize legacy workflows to support continuous monitoring and platform integrity.

Build and operate scalable data pipelines, ingestion, transformation, and storage to support LLMs and ML systems. Create tooling for data quality, monitoring, and observability. Collaborate with engineering, product, and ML teams to enable experimentation, training, evaluation, and reliable production AI features in a fast-moving startup.

Own end-to-end clinical data ingest and result delivery for a radiology AI product: on‑prem C‑STORE ingest, cloud DICOMweb STOW-RS handling, tenancy/privacy at the boundary, outbound HL7/JSON delivery, per-study tracing and health checks, CI for gateway repos, and precise security-review documentation. Troubleshoot protocol, networking, and memory/streaming issues using packet captures and profiling.

Build and scale optimized data pipelines and data engine architecture to run ML models, reduce annotation costs, integrate foundation models for labeling and QA, use software/hardware-in-the-loop testing, and collaborate with DoD customers to understand requirements and support field events.

Build and own highly-available data pipelines that move robot video, telemetry, and demonstration data from edge to training sets. Architect ingestion, streaming (MoQ/WebTransport), storage, indexing, and retrieval; improve throughput, reliability, and cost; and build tooling for data discovery and trust while working closely with ML and robotics teams.

Lead architecture and implementation of agentic AI systems powering Zuma's platform. Rebuild onboarding and integration frameworks, implement analytics and observability for LLM performance, translate product needs into technical solutions, mentor engineers, establish engineering standards, and work directly with customers to enable scalable, production-grade AI experiences.

Lead end-to-end ML implementations for clients, design and maintain production-grade MLOps pipelines, deploy and optimize Generative AI/LLM and NLP solutions, manage infrastructure (Docker, orchestration, distributed compute), and communicate technical results to stakeholders.

The role involves designing and building systems to process and operationalize data, creating AI agents, managing data pipelines, and developing admin tools. Responsibilities include architecture design and rapid problem-solving in a small team environment.

Lead design and implementation of Mercor's code search and retrieval systems: hybrid dense-code embeddings plus lexical (BM25) retrieval, candidate generation, ranking/re-ranking, model routing, query translation from natural language, indexing pipelines, evaluation/A-B testing, and technical leadership to evolve embeddings and search quality safely at scale.

Own design, build, and reliability of a cloud-native data lakehouse (Bronze->Silver->Gold). Build scalable ETL/ELT pipelines (ADF, Airflow), optimise Snowflake, develop dbt models, embed data quality/observability (Elementary), manage ADLS Gen2 and Azure serverless components, and drive infra-as-code, CI/CD, and cross-team collaboration.

The Senior Data Engineer will build and maintain data pipelines, distributed ETL/ELT workflows, and data storage systems while collaborating with various teams to optimize data quality and performance.