Top Data Engineer Jobs in Portugal

Leads the architecture and production engineering of Arlo’s AI-native data platform. Builds governed data products, semantic and metric layers, catalogs, metadata, lineage, access controls, ML capabilities, LLM interfaces, and safe cross-store query patterns. Ships production code, tests, CI, and operational systems while driving adoption across Data Engineering, Analytics, Cloud Infrastructure, and Enterprise Applications. Requires deep experience with lakehouse architecture, heterogeneous data integration, AWS, enterprise data modeling, AI/LLM systems, and platform ownership.

Build, automate, and operate Azure-based enterprise data platform infrastructure. Responsibilities include Infrastructure as Code, CI/CD, Databricks administration, Azure data services, networking, security, monitoring, incident response, governance, FinOps, and reliability engineering. The role develops deployment standards, manages cloud environments and access controls, optimizes costs, supports data engineering teams, and documents operational procedures. Collaboration spans Data Engineering, IT Infrastructure, Cybersecurity, and business stakeholders.

Own quality assurance and auditing for multimodal data sourced from external vendors. Build scalable software tooling and infrastructure for sampling, review workflows, quality checks, audit trails, and vendor reporting. Define measurable data standards with researchers, manage vendor feedback loops, and improve data quality through instrumentation and iterative processes. The role also requires hands-on sample review, rubric development, cross-functional communication, and approximately five years of customer-facing software experience.

Develop and optimize Ray Data, a Python-native distributed data processing engine for AI workloads. Improve performance, scalability, data loading, stability, and fault tolerance across heterogeneous environments. Build production training data solutions and collaborate with customers and AI-native companies to scale large machine learning workloads.

Architect, build, and evolve AWS-based data platforms and pipelines handling large, complex datasets. Optimize data solutions for scalability, reliability, performance, and cost efficiency. Collaborate with engineering and product teams, make architectural decisions, establish best practices, provide technical mentorship, and support modernization toward intelligent data architectures. The role involves Python, SQL, Databricks, Kubernetes, automation, DevOps, and agentic AI technologies.

Leads the architecture, development, operation, and evolution of PlayStation’s real-time analytics platform. Builds scalable distributed data systems using stream processing and high-performance analytics technologies, integrates batch and streaming workloads, applies AI/ML to improve platform operations, and drives reliability and performance. The role owns critical platform capabilities, establishes technical standards, mentors engineers, influences architecture across teams, supports production incidents, and shapes long-term data platform strategy.

Leads data and analytics engineering for Zero Trust maturity assessment and implementation measurement. Designs common data models, integrates and validates enterprise data, develops maturity and risk metrics, supports prioritization and gap analysis, and establishes data quality, lineage, traceability, and documentation practices. Partners with dashboard and automation specialists to streamline reporting and data refresh workflows.

Design, build, and optimize SpaceX’s high-throughput AI inference platform. Responsibilities include distributed model serving, GPU and inference-engine optimization, request routing, autoscaling, caching, observability, CI/CD, SDK development, reliability, and production performance tuning. The engineer will collaborate across AI infrastructure teams and support internal training workloads. This is an onsite role in Palo Alto.

The Senior Data Engineer will design and develop scalable data architectures, manage ETL processes, optimize databases, and implement MLOps pipelines, promoting data-driven decision-making across the organization.

Lead enterprise data integration and Snowflake platform engineering. Build and optimize Snowflake data models and ELT pipelines (dbt/Python), enable MDM (Reltio) integrations, implement data quality, lineage, and cost/performance optimizations, and partner cross-functionally to deliver AI-ready and analytics data products.

Build procedural generators that produce parametric, hardware-like geometries and physically plausible simulation setups (materials, boundary conditions, loadings). Implement programmatic CAD workflows, geometry processing and meshing pipelines, measure dataset diversity/coverage, and iterate dataset composition to improve physics ML model performance.

Own and scale streaming Kafka pipelines and large-scale Spark batch jobs processing billions of rows. Drive pipeline reliability, observability, data modeling, and cost/latency optimizations. Partner with data scientists and platform engineers, define SLOs, improve efficiency, contribute to shared infrastructure (Airflow, libraries, data catalog), and mentor junior engineers.

Design, implement, and maintain scalable Data Lake architectures; integrate Data Lake with Stardog knowledge graph; optimize ETL/ELT and Delta Lake pipelines; enforce DoD IL-4/IL-5 security, IAM/RBAC, and Unity Catalog governance; produce compliance and performance reports; support DevSecOps, CI/CD, and cloud integrations across AWS/Azure/GCP.

Build and operate large-scale data infrastructure for robot learning: ingest, validate, transform, and featurize multimodal data; run batch and streaming workflows; design storage and metadata systems; optimize training-time performance; manage data movement and lifecycle; implement observability and guardrails; collaborate with researchers and engineers.

Design, develop, and deliver real-time data replication solutions using IBM IIDR across mainframe and midrange platforms. Lead end-to-end IIDR pipelines, optimize performance, troubleshoot complex replication issues, automate releases and tests, mentor engineers, and ensure production stability and CI/CD readiness.

Hands-on Site Reliability Engineer responsible for building and maintaining cloud infrastructure, CI/CD pipelines, observability (logging/monitoring/tracing), automation, and security best practices. Manage datacenter resources, troubleshoot clusters and services, collaborate with engineering teams for deployments, and participate in on-call incident response to ensure high availability and performance.

Design, implement, and maintain automated ingestion and transformation workflows to bring spreadsheet-based operational data (JSON/CSV/Excel) into enterprise databases and Pega workflows. Validate data integrity, troubleshoot pipelines, support reporting integrations, document data flows, and collaborate with architects, developers, and stakeholders to modernize and secure enterprise data services.

The Senior Data Engineer will design, implement, and maintain data pipelines, improve data quality, and support investment data management, collaborating within a team to enhance decision-making and reporting capabilities.

Lead design, build, and productionize scalable ML systems on cloud platforms. Provide technical leadership, mentor teams, translate business needs into data-driven solutions, work with large datasets, implement recommender/NLP/vision models, leverage LLMs and agentic architectures, and support business development and client engagements.

Design, develop, and productionize scalable machine learning and data science solutions for clients. Analyze very large datasets across cloud platforms, implement recommender/NLP/computer vision and LLM-based systems, optimize for performance and scalability, leverage containerization and data warehousing, and lead/mentor junior practitioners while supporting business development and client strategy.

Lead design and delivery of end-to-end, production-scale ML systems on cloud platforms. Provide technical leadership, mentor teams, analyze and transform large datasets, implement recommender/NLP/risk models, productionize scalable ML, and grow practice through client-facing activities and business development.

Design, build, and productionize scalable machine learning systems; analyze and transform large datasets; implement recommender, NLP, and other analytic solutions; deploy models in cloud/enterprise environments; collaborate with clients and lead junior team members while contributing to business development.

Lead SRE and production support for big data services: manage incident/problem management, monitoring/observability (AppDynamics, Splunk, ELK, Grafana), service improvement, automation (Ansible, CI/CD), disaster recovery, capacity/configuration/change management, and coordinate with development, architecture, and infrastructure teams while leading L2 service delivery and on-call support.

Provide production application and Big Data platform support for critical financial systems: monitor, troubleshoot, tune Hadoop/Cloudera clusters, manage incidents and releases, maintain runbooks, perform risk reconciliations, liaise with stakeholders and vendors, drive stability and SRE best practices, and coach junior staff.

Designs and operates scalable serverless AWS data pipelines, data models, ETL/ELT workflows, feature stores, and analytics-ready data assets. Partners with AI, Analytics, Product, and Underwriting teams to support reproducible ML training and scoring, MLOps, data quality, monitoring, governance, and production troubleshooting. The role handles sensitive healthcare and financial data, optimizes serverless workloads, supports batch and near-real-time processing, and applies infrastructure-as-code, CI/CD, documentation, and responsible data-use practices.