Top Data Engineer Jobs in Portugal

Own and build the end-to-end data layer: design high-throughput real-time ingestion, architect petabyte-scale open table formats, implement and optimize Spark-based batch/stream pipelines, enable Kafka/Flink integrations, ensure performance/reliability/cost efficiency, define data contracts and schemas, and establish infrastructure best practices on AWS GovCloud, Kubernetes, and Airflow.

Lead and architect large-scale data products and pipelines across teams. Design and deliver ELT/ETL solutions, evaluate tools, ensure scalability and integrity, influence technology decisions, and research emerging big-data/cloud methodologies (AWS, Hadoop/EMR, Spark, Kafka, Snowflake). Partner with architects and stakeholders to drive implementation and operationalize production workloads.

Design, build, and maintain data ingestion, ETL, storage, and analytics systems to measure and scale Starlink network performance (including Wi‑Fi). Automate metric aggregation, detect widespread application issues, lead investigations, fuse multi-source data, and apply analytics and ML models to vehicle- and ground-generated data.

Design and build the data and analytics layer: schemas, pipelines, APIs, dashboards, forecasting and ML models. Deliver usage-based metering, ensure data correctness and observability, and ship customer-facing analytics while raising engineering standards.

Design, build, and maintain near real-time manufacturing dashboards and decision-support tools. Translate plant needs into intuitive visualizations, create reusable components, ensure data reliability and performance, collaborate with OT/IT and operations, and provide documentation, training, and governance-aligned deployment.

Build and maintain reliable Apache Airflow pipelines and workflow engines to move, schedule, and monitor data. Develop automation using Bash, Python, and Java. Work with distributed processing (Spark), containerization (Docker/containerd/Podman), and Git source control to support analytics and operational needs.

Design, build, and maintain interactive Power BI dashboards (including Fabric workspaces). Analyze data to identify trends, ensure data quality, collaborate with stakeholders to gather requirements, and present actionable insights to support decision-making.

Lead data engineering projects across cross-functional teams. Design resilient batch and real-time analytics systems, optimize pipelines and infrastructure, integrate ML models, and provide technical leadership, coaching, and mentoring to improve reliability, scalability, cost-efficiency, and maintainability.

Design, build, and deploy ML systems that link extracted document features to repayment and fraud outcomes. Run backtests, build evaluation and QA frameworks, develop adaptive/online learning for fraud and credit risk, and collaborate with customers to integrate models into underwriting workflows.

Design, build, and maintain large-scale data pipelines and APIs for Ventura OS. Optimize data processing workflows, integrate ML models into production, ensure data quality and observability, and collaborate across teams to drive architecture and data infrastructure decisions for streaming and content recommendation use cases.

Define and lead the technical architecture for a lakehouse on AWS, build production pipelines and data models for time-series and multi-source data, introduce data governance and catalog foundations, author platform playbooks (ADRs, runbooks, Terraform modules), deliver projects end-to-end, and mentor engineers while representing the platform to senior leadership and non-technical stakeholders.

Design, develop, QA, and maintain BI dashboards using Qlik to transform structured and unstructured data into actionable visualizations. Partner with clients to explore, clean, analyze data, build user-centric UI elements, and communicate insights to technical and non-technical stakeholders.

Design and lead data science solutions: ingest and analyze structured and unstructured data, build predictive models and NLP systems, integrate enterprise tools, and advise clients while guiding teammates on AI/ML workflows and deployments.

Build information-rich legal data visualizations using JavaScript, React, D3, and SVG. Use Python and Pyramid to retrieve and prepare data. Design for accessibility and usability, integrate new legal content with subject-matter experts, collaborate with frontend/back-end and NLP engineers, and participate in code reviews. Provide a portfolio of reviewable code samples.

Lead design and implementation of the enterprise data platform: architect scalable warehouse/lakehouse solutions, build and optimize ETL/ELT pipelines, enforce data quality and governance, tune analytical workloads, and enable reporting and self-service analytics in support of BI and AI initiatives.

Maintain and evolve the data ecosystem for fraud detection and analytics: build and operate ELT/ETL pipelines, integrate multiple data sources, implement monitoring/alerting, automate processes, collaborate with data scientists and commercial teams, mentor junior engineers, and communicate technical work to non-technical stakeholders.

Design and operate real-time and batch data pipelines on Azure and Databricks for IoT/telemetry. Build ETL/ELT workflows, streaming ingestion, data services/APIs, and ensure data quality, privacy, performance tuning, and CI/CD for analytics and ML consumption.

Build core data and AI infrastructure to ingest, model, and serve unstructured healthcare policy data. Design backend systems, data pipelines, LLM-powered RAG/embedding pipelines, evaluation frameworks, and platform APIs. Improve data quality, auditability, and system reliability while iterating rapidly with founders.

Designs and develops cloud-native ETL/ELT solutions and data warehouses using Informatica, Python, Oracle/PLSQL and scheduling tools. Architects data pipelines, optimizes SQL performance, automates jobs, supports testing and implementation, and documents applications across multiple projects.

Design, build, and operate petabyte-scale data infrastructure: real-time ingestion, storage (open table formats), and serving. Lead performance, reliability, and cost optimization across Spark and streaming pipelines (Kafka/Flink), define data contracts, and establish best practices and tooling. Collaborate with platform, product, and deployment teams to deliver production-grade data systems on AWS GovCloud, Kubernetes, and Airflow.

Design, build, and operate high-throughput real-time data integration and storage systems. Architect open table format layers at petabyte scale, build and optimize Spark-based batch and streaming pipelines, manage stream processing (Kafka/Flink), orchestrate pipelines (Airflow), and ensure performance, reliability, and cost efficiency across the data stack. Collaborate with platform and product teams and own end-to-end data infrastructure.

Design, build, and operate petabyte-scale data infrastructure: real-time ingestion, storage (open table formats), and serving. Architect scalable Iceberg-based data lakes, optimize Spark pipelines and streaming (Kafka/Flink), drive reliability, cost efficiency, and data contracts; collaborate with product and platform teams and establish best practices and tooling.

Design and build core data governance and access-control systems for a multi-tenant data platform. Implement fine-grained permissions, auditability, policy enforcement, metadata management, data labeling, and compliance. Collaborate with product, architect scalable secure systems, drive projects end-to-end, and mentor senior engineers to elevate engineering and security practices.

Architect and build core data governance systems (fine-grained permissions, auditability, policy enforcement, metadata, labeling, compliance) for a multi-tenant platform. Drive projects end-to-end with Product, balance security and usability, own large components, mentor engineers, and ensure scalable, secure handling of billions of data points.

Design and build scalable, semantically consistent 360 entity data models and transformation pipelines. Implement cleansing, enrichment, KPIs, scoring, and business rules in SQL and Python/Scala, ensure data quality, lineage, and performance at scale. Use AI coding assistants and SDD to accelerate development, create tests and documentation, and collaborate with domain experts, data scientists, and product teams to deliver trusted, reusable data assets.