Top Data Engineer Jobs in Portugal

Lead a large engineering organization to design, pilot, and roll out reference architectures and AI-ready data platform capabilities. Build and operate cloud-native data pipelines, feature and vector stores, observability and governance standards, and mentor senior technical leaders while driving enterprise-wide architectural modernization.

Build and maintain scalable enterprise data models, ETL/ELT pipelines, and data-processing solutions using Azure, Databricks, SQL, and Python. Support analytics and AI/ML workflows, implement data security and compliance for PHI/PII, ensure data quality and audit readiness, and create dashboards using Power BI or Tableau. Collaborate with cross-functional stakeholders to deliver governed, reliable data products and AI-powered solutions.

Participate in Celonis’s 12-month Orbit Program, consulting with customers, identifying business challenges, and developing AI-driven prototypes using SQL, PQL, machine learning, and LLMs. Lead proof-of-concept projects, present solutions to executives, quantify business value, build customer relationships, and partner with implementation, product, and engineering teams to drive value realization.

Build and operate ETL and real-time pipelines connecting veterinary practice systems, billing platforms, and third-party APIs. Design data models and a warehouse supporting hospital analytics, finance, and reporting. Establish standards for testing, documentation, monitoring, and data quality, while troubleshooting production data issues. Partner with Finance to improve Sigma self-service reporting and define scalable integration patterns as the company’s first data engineer.

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.

Build and maintain large-scale data ingestion platforms, pipelines, integrations, and monitoring tools for Redfin’s real-estate inventory data. Develop applications using relational and non-relational databases, spatial data, Kafka, Java, Python, Kubernetes, Jenkins, Airflow, and AWS. The role involves architecture, technical leadership, mentoring, data quality improvements, production support, and possible on-call participation.

Lead design and delivery of cloud-native, AI-powered data platforms, copilots, and enterprise-scale services. Architect LLM/RAG/agentic solutions, build AI-ready data foundations, create reusable frameworks/SDKs, establish Responsible AI and governance practices, and provide hands-on technical leadership and cross-team partnership to drive AI adoption and scale.

Design and maintain data pipelines, ETL workflows, and graph database models. Build RAG and GraphRAG pipelines for generative AI applications using graph and data platforms. Collaborate with data engineers and scientists, engage client stakeholders, gather requirements, present progress, and translate business needs into technical solutions within a cross-functional consulting team.

Design and deliver cloud-native data platforms, scalable big data pipelines, lakehouses, warehouses, transformation frameworks, BI solutions, and ML-enabled data workflows. Lead client-facing pre-sales engagements, technical workshops, proof-of-concepts, executive conversations, and enterprise data modernization initiatives. Build reusable architectural IP, influence senior stakeholders, and translate ambiguous business challenges into production-ready solutions using AWS and modern data technologies.

Designs and maintains Snowflake-based cloud data pipelines and lake architectures, develops scalable ETL/ELT workflows, and supports legacy-platform migrations. Ensures data cleaning, reconciliation, validation, and financial data integrity. Translates business requirements into Jira epics and user stories, supports project planning, testing, cutover, and go-live readiness, and coordinates with business, finance, operations, and technical stakeholders.

Build and maintain data pipelines to ingest, normalize, and deliver messy healthcare data (FHIR, HL7, CCDAs, PDFs) into structured models for AI and clinical use. Implement ETL, real-time webhooks, document extraction, PII sanitization, storage on AWS, and monitor data quality while ensuring compliance with healthcare interoperability standards.

Design, build, and maintain scalable batch and streaming data pipelines using Python, SQL, Kafka, and GCP. Develop REST and GraphQL data services, CI/CD automation, observability, and data-quality frameworks. Integrate ML and NLP models into production workflows, including continuous retraining and real-time inference. Support structured and unstructured healthcare datasets, distributed systems, microservices, and reliable data infrastructure while collaborating with data science and MLOps teams.

Supports cybersecurity data-source onboarding and maintains Cribl and Databricks pipelines for collection, transformation, enrichment, normalization, validation, and delivery. Develops Python and SQL automation, troubleshoots ingestion and schema issues, monitors pipeline performance, documents data flows and procedures, and supports integrations with security and analytics platforms. Collaborates with engineers, analysts, data scientists, and mission stakeholders in classified and unclassified environments.

Build and configure a government Databricks workspace, develop ingestion and integration pipelines from enterprise and legacy systems, and prepare data for reporting. Implement Unity Catalog governance, metadata management, auditing, and lineage; configure MLflow workflows; assess security controls for sensitive data; and create architecture documentation, data dictionaries, and SOPs to support long-term sustainment.

Build reusable Python libraries and reproducible analysis pipelines for image and experimental data; define data models and provenance; partner with wet‑lab scientists to translate requirements and validate methods; establish testing, validation, versioning, and observability for scientific workflows.

Build systems that capture professional design activity and convert it into training-grade preference data. Own product instrumentation, pipelines from canvas to warehouse to training sets, dataset contracts, lineage, reproducibility, privacy boundaries, consent, and feedback collection. Partner closely with product engineers and researchers to ship new signals, make ambiguous behavioral data accurate, and ensure datasets and training results are traceable and repeatable.

Develop, integrate, test, deploy, secure, and sustain enterprise software and data solutions for Air Education and Training Command. Responsibilities include web applications, APIs, data pipelines, Palantir Foundry and Envision solutions, data quality, testing, release support, Agile delivery, incident management, security, technical documentation, and stakeholder engagement.

Designs and maintains scalable data pipelines, warehouses, lakes, integration solutions, and analytics platforms. Develops AWS and Snowflake data solutions using Python, SQL, dbt, ETL/ELT, and Data Vault modeling. Supports replication, APIs, governance, metadata, lineage, and data quality across enterprise environments. Troubleshoots complex data issues and provides technical leadership while collaborating with engineering, architecture, analytics, and business teams.

Lead the design and optimization of healthcare data pipelines integrating EHR, payer, HIE, and vendor data. Build AWS-based ETL/ELT infrastructure, reusable integration tooling, data quality monitoring, and ML enablement foundations. Ensure HIPAA-compliant handling of clinical data, including security controls, audit logging, encryption, and de-identification. Provide technical leadership, mentor engineers, influence platform architecture, and lead complex partner integrations involving FHIR, HL7v2, CCDA, and healthcare terminology standards.

Build, scale, and optimize ETL and ELT data pipelines using Databricks, PySpark, Python, SQL, and distributed processing technologies. Collaborate with data engineering, AI, governance, and reporting teams to support analytics and reporting outcomes. Contribute to cloud data architecture, AI integration, DataOps, MLOps, and modern data platform capabilities including Unity Catalog, workflows, notebooks, medallion architecture, and Delta Lake.

Build and optimize scalable ELT/ETL pipelines, data models, schemas, and monitoring systems in BigQuery. Ensure data quality, governance, compliance, and pipeline reliability while collaborating with data scientists, analysts, engineers, and business partners. Develop generative AI data products and document engineering standards. The role requires cloud data experience, SQL, programming, orchestration tools, and strong communication skills.

Supports the development and maintenance of data pipelines, models, ETL workflows, and data quality processes for marketing analytics and CRM solutions. Writes Python, SQL, or Scala code, monitors and troubleshoots data flows, integrates data from multiple sources, and collaborates with engineers, analysts, data scientists, CRM developers, and product owners. Learns cloud data architecture, migrations, and modern data management practices.

Partner with US Risk Management to design and deliver solutions: build Databricks ETL jobs, automate workflows with Power Platform, create data flows using Azure Data Lake, set up DevOps environments, and support AML Tech. Follow DevSecOps practices, document SDLC and change requests in ServiceNow, and collaborate with stakeholders to align software with banking risk requirements.

Build and operationalize scalable, cloud-native data pipelines supporting AI, machine learning, and business intelligence. Design data solutions, transform and cleanse complex data, and implement governance, security, and quality practices. Develop production-grade multi-agent AI applications using orchestration frameworks, tool-calling, MCP, RAG, vector databases, and embeddings. Analyze and resolve data defects, test pipelines, collaborate across teams, educate users, and help lead other engineers.

Design, build, and maintain scalable data pipelines, data lakes, and databases; ingest and map customer financial datasets; monitor pipeline reliability; translate business requirements into data flows and analytical insights to ensure high-quality, usable data.