Top Data Engineer Jobs in Portugal

The Senior Data Engineer II will build and optimize data solutions on Axon's Corporate Data Platform, design data pipelines, mentor team members, and uphold data governance and quality standards.

Design, build, and maintain data pipelines and Azure data warehouse infrastructure. Ensure data quality, governance, performance tuning, automation, and provide BI insights using Power BI or QlikSense.

Intern will explore and clean unfamiliar datasets, write code to ingest, transform, and monitor data quality using Python, SQL, and OCaml, work with structured and unstructured data, complete two owned projects, and be mentored by full-time Data Engineers.

Design and build a modern, scalable data platform (warehouse, lakehouse, or hybrid) to support analytics, data science, and near-real-time processing. Lead platform architecture, CDC-based ingestion, ETL/ELT pipelines, data quality/observability, and governance. Partner cross-functionally, provide technical leadership and mentorship, and modernize legacy data processes for low-latency enterprise reporting and ML workloads.

Lead technical pre-sales engagements for Microsoft cloud database and analytics products. Deliver PoCs, demos, hackathons, and architecture workshops; design secure, scalable solutions; resolve technical blockers; collaborate with engineering; and evangelize Fabric, Databricks, Purview and Azure database technologies to accelerate customer migrations and adoption.

Design, deploy, automate, and support Cloudera Data Platform clusters and Hadoop ecosystem services. Build Ansible automation and CI/CD for provisioning, configuration, patching, monitoring, security integrations (Kerberos/LDAP/TLS), capacity planning, disaster recovery, and on-call operations to support data ingestion, processing, and analytics workloads.

Design and deliver high-value human data and evaluation projects for LLMs. Partner with model teams, build tooling to measure data effectiveness, maintain data integrity, design annotation workflows, run evaluations, and scale Human Data operations to improve model quality and truthfulness.

Lead data engineering teams to design, build, and maintain scalable cloud-native data pipelines, data platforms, and data products using Snowflake, Databricks, Airflow, and modern architectures. Optimize ETL/ELT, SQL, and transformations for analytics, reporting, and AI use cases; ensure data quality, governance, observability, and stakeholder alignment. Produce documentation, runbooks, and present technical solutions to business and technical audiences.

Serve as first point of contact for customers using Agentforce and Data 360, troubleshoot technical issues, provide feature guidance and best practices, manage customer expectations, create and maintain knowledge content, participate in training, and escalate complex issues to internal teams to ensure customer success.

Design, build, operate batch and real-time data pipelines and ETL/ELT solutions on cloud platforms. Optimize data models, warehouses/lakehouses, ensure data quality, security, and performance, and collaborate with analysts, data scientists, and stakeholders to deliver trusted datasets.

Lead a team building large-scale ML model evaluation and orchestration frameworks for autonomous vehicles. Own data analysis workflows, dataset creation, statistical model evaluation, regression reporting, and tool development (AWS Kubernetes orchestration, ReSimulation). Provide technical leadership, mentorship, and manage project timelines and deliverables.

As a Senior Controls Engineer, you will design, deploy, test, and maintain control systems, ensuring quality through collaboration with clients and team members.

Build and operate an AWS-native data lake and AI gateway. Implement and maintain lakehouse architecture (Iceberg) using Glue, Athena, and S3; manage infrastructure as code (Terraform); run reliable data pipelines; operate LLM gateway/Bedrock; enforce security, IAM, networking, and secrets controls; partner with IT and Security.

Design, develop, and maintain scalable ETL/ELT pipelines and Snowflake data models; optimize performance; ensure data quality and integrations with CRM/ERP/billing systems; collaborate with stakeholders and evolve a Snowflake/dbt cloud-native data platform.

Assist the data engineering team in building, testing, and documenting data pipelines and storage. Work with Python, SQL, and Git on time-sensitive tasks, execute unit tests, support senior engineers, and complete assigned projects (part-time, 20 hr/week).

Design, develop, maintain, and test data infrastructures and pipelines; optimize data flow and collection; collaborate with data science to integrate models; analyze IT environments; deliver data solutions for stakeholders and support project milestones.

Build and maintain high-throughput C++ recording systems and Python data services for vehicle sensor and telemetry data. Work on data transfer, ingestion, validation, format/versioning, indexing, compression, and efficient access for replay, analytics, and ML while operating under vehicle resource constraints and contributing to quality management.

Own and scale the usage-to-billing pipeline and real-time CRM ingestion, design the customer/commercial graph, work across front-end and backend ingest infrastructure, and mentor engineers while shaping system architecture and operational reliability.

Design, build, and maintain backend systems and data interactions for healthcare reporting. Develop APIs, data models, ETL pipelines, and interfaces; optimize performance, ensure data integrity and security, and support automated testing and CI/CD. Collaborate in Agile teams and reduce technical debt while enabling integrations with frontend and third-party systems.

Design, build, and maintain scalable data infrastructure and pipelines for large, varied basketball and performance datasets. Lead technology decisions, implement medallion architectures and data validation, optimize storage and ingestion, and collaborate with cross-functional stakeholders to deliver analytics and application-ready data.

Design, build, and maintain scalable, secure data pipelines and platforms to acquire, transform, and store large financial datasets. Enable AI/GenAI-ready data foundations for model training, inference, retrieval, and RAG workflows. Collaborate with cross-functional teams, ensure data quality, governance, observability, and compliance, and support production deployments and troubleshooting.

Lead the end-to-end Document Crunch data platform: migrate Redshift to Snowflake, build ingestion and transformation pipelines, define data models, enforce data quality and observability, integrate CRM/MDM and Customer 360, enable analytics and AI-enabled data products, and act as the primary technical liaison to the Trimble Data Team to codify enterprise-aligned, code-driven M&A data integration.

Design, develop, and optimize large-scale distributed data systems for real-time and batch processing. Support feature engineering and feature store evolution, build identity graph capabilities, improve observability and performance, and mentor engineers while collaborating with Product, ML, and Data Science teams.

Build and manage automated ETL integrations from customer EMRs, billing, and warehouses into Caris systems. Lead data discovery, validation, and architectural design for clinical informatics and BI. Gather technical requirements, implement access via FHIR/HL7/APIs or direct DB, assemble large clinical datasets, and deliver dashboard-ready data for business users.

Design and lead data lake architecture and Databricks platform solutions for DoW projects. Develop and optimize ETL/Delta Lake pipelines, integrate AI/ML workflows, implement DevSecOps/CI-CD, ensure cloud security and compliance across AWS/Azure/GCP, and liaise between clients and developers to deliver solutions.