Top Data Engineer Jobs in Portugal

Build and maintain marketing data pipelines, replace spreadsheet workflows with structured databases, automate data cleaning/deduplication and syncing, develop AI-driven reports and dashboards, and collaborate with marketing, AdOps, and engineering to ensure reliable, unified, AI-ready marketing data.

Design and implement data ingestion, transformation, and Foundry applications using Palantir Foundry AIP. Develop workflows in Workshop and Ontology Manager, integrate AI/ML models, support analytics and mission-focused solutions, collaborate with stakeholders, follow Agile and DevSecOps practices, and manage Git/CI/CD pipelines. Role requires eligibility for Secret clearance and a bachelor's degree.

Design, build, and maintain secure, scalable data pipelines and ML data workflows to support IRS fraud detection and compliance analytics. Collaborate with data scientists and stakeholders, troubleshoot production issues, produce documentation and briefings, and operate in restricted government environments.

The Senior Data Engineer will design and build data systems, mentor engineers, and collaborate with product teams to optimize data architecture and pipelines for scalability and reliability.

The Data & AI Engineer III will lead cloud data engineering solutions, develop data infrastructure, establish architectures, mentor team members, and secure data around cloud systems.

The Staff Business Systems Engineer will manage systems supporting Data Center operations, focusing on capacity planning, lifecycle management, integrations, and asset tracking across infrastructure and finance. Responsibilities include architecture design, data modeling, and ensuring operational compliance.

Design and operate backend systems for high-volume, multi-stage data pipelines that de-identify sensitive enterprise data. Own versioned contracts, lineage, state transitions, retries, replay, reconciliation, verification, migrations, and safe recovery. Diagnose production behavior across services and storage, improve correctness and throughput, and build auditable release controls. Collaborate with ML, data, platform, security, product, and customer-facing teams while using AI tools with deterministic safeguards and human review.

Develops and deploys predictive analytics, machine learning, and deep learning solutions for production use. Responsibilities include statistical analysis, feature engineering, model development, anomaly detection, NLP, computer vision, MLOps, CI/CD pipelines, cloud AI platform integration, data engineering collaboration, model monitoring, governance, and stakeholder communication. The role also supports large-scale analytics, AI modernization, and human-reviewed multi-source summarization.

Design, build, and operate scalable batch, streaming, and CDC data pipelines across Snowflake and AWS. Lead ETL/ELT architecture, orchestration, data modeling, quality monitoring, observability, incident resolution, and performance optimization. Develop production solutions in SQL and Python, establish engineering standards for testing and CI/CD, and partner with Analytics Engineering, Data Science, Product, and Engineering teams to deliver reliable data products.

Lead design and evolution of FloQast's core data platform and lakehouse, owning event streaming, batch and streaming ETL, storage, governance, and compute architecture (Spark). Define standards, mentor engineers, collaborate cross-functionally, and ensure scalable, secure, production-grade data infrastructure for product and analytics.

Build scalable data solutions and reconciliations to automate Compliance & Finance regulatory processes. Partner with stakeholders to translate requirements into designs, lead technical development, review code, and evolve standards for data pipelines and reporting.

The role involves designing and building data pipelines for analyzing user feedback on AI models, ensuring data quality and infrastructure scalability.

Leads architecture, optimization, scaling, and ownership of Zoox’s enterprise and AI data platform. Builds self-healing pipelines, governance frameworks, data quality and observability systems, secure access controls, lifecycle policies, and automated CI/CD deployment infrastructure. Optimizes Databricks and AWS EMR performance and costs while enabling low-latency, AI-ready data foundations. The role also drives enterprise security, compliance, schema evolution, and developer velocity, with opportunities to apply GenAI, Terraform, and EKS.

Develop and govern MBSE and SysML digital models for defense and space mission systems while applying Python, SQL, statistics, visualization, and machine learning to engineering and operational data. Establish digital-thread traceability, data architectures, requirements relationships, technical baselines, and explainable analytics. Support trade studies, capability assessments, AI/ML evaluation, architecture decisions, Agile and DevSecOps delivery, and collaboration with systems engineers, architects, operators, cybersecurity teams, and government stakeholders.

Design and implement a financial knowledge graph architecture, build graph ingestion and transformation pipelines, apply graph algorithms and analytics, define best practices, drive adoption across teams, and mentor/hire engineers to scale the platform while collaborating with investment and compliance stakeholders.

Leads technical discovery, solution design, RFP responses, demonstrations, pilots, proof-of-concepts, and data testing for enterprise marketing and media clients. Acts as a trusted advisor on data, privacy, identity, attribution, and activation, translating technical concepts into business decisions. Partners with Sales, Research, and Product teams while influencing platform enhancements and supporting client implementations.

Designs and enhances an enterprise data and AI platform, building reusable AI starter kits, reference implementations, templates, and developer-focused patterns. Partners with engineering, governance, and product teams to embed governance, accelerate AI adoption, and reduce delivery time. Leads enablement programs, champions cloud-first innovation, advises cross-functional initiatives, and mentors teams developing and deploying AI solutions. Requires expertise in AI/ML, GenAI, cloud data products, Snowflake or Databricks, agile delivery, and enterprise software development.

Designs and operates data-driven solutions supporting intelligence requirements. The role develops data architectures, integration models, automation, algorithms, and software tools; collaborates with GEOINT analysts; manages data governance and metadata; interrogates databases with SQL; and applies data science libraries to large-scale intelligence analysis. It requires substantial intelligence and GEOINT experience, advanced technical expertise, strong communication skills, U.S. citizenship, and an active TS/SCI clearance.

Designs, builds, and operates scalable batch and real-time data infrastructure processing trillions of data points. Responsibilities include platform architecture, infrastructure as code, self-healing systems, observability, developer tooling, pipeline optimization, security controls, database tuning, and distributed-system troubleshooting. The role collaborates with engineering and product teams, improves reliability and performance, and enables safe access for data engineers, software engineers, and AI agents.

Design, build, and maintain scalable data pipelines and platforms to ingest, merge, and prepare structured and unstructured data for analytics and AI. Lead multidisciplinary teams, perform data exploration and statistical analysis, and support use of LLMs/GenAI to enable mission-driven solutions across fraud detection, healthcare, and intelligence domains.

Build and operate large-scale data ingestion pipelines powering research and trading platforms. Own pipeline quality, reliability, performance, and data health; collaborate with traders, quantitative researchers, and data owners; optimize compute, storage, and SLAs; migrate pipelines to modern platforms; and implement tools to detect and resolve source-data quality issues.

Design, build, and operate Databricks Lakehouse pipelines and Delta tables. Optimize Spark/SQL workloads, implement incremental/CDC patterns, ensure data quality, deliver documented datasets and dashboards (Power BI), and collaborate with stakeholders to meet SLAs while supporting data-driven decisions.

Design and implement secure, resumable cloud pipelines to ingest multi-terabyte medical imaging data; build GPU-accelerated orchestration with strong retry semantics and cost controls; define data lifecycle and retention; implement HIPAA/PHI security and compliance controls; and deliver operational tooling (monitoring, alerting, runbooks) for a device fleet.

The candidate will improve the data ETL pipeline, build analytical tools, manage operations, and expand into new markets and asset classes.

Build and operate ingestion and transformation pipelines to turn messy insurance data into clean datasets. Design bronze/silver/fact layers, model and tune Postgres and analytical stores, define KPIs and reconciliation checks, orchestrate observable data jobs, and ensure end-to-end data quality for analytics and AI products.