Top Data Engineer Jobs in Portugal

Designs, develops, and maintains IBM Planning Analytics and TM1 models, cubes, applications, rules, feeders, and TurboIntegrator processes for financial planning, forecasting, reporting, and actuarial modeling. Builds data ingestion, transformation, quality, masking, and curation processes; supports SQL development, security, performance monitoring, and critical data pipelines. Partners with finance and actuarial stakeholders, provides operational support, develops scalable tested code, and mentors team members.

Leads an engineering team building scalable ML data infrastructure, including data ingestion, processing, storage, access, pipelines, and analytical platforms. Defines architecture, evaluates technologies, improves performance and reliability, translates data requirements into technical solutions, and ensures governance, security, and regulatory compliance. Provides mentorship, strategic roadmap input, and cross-functional collaboration with ML engineers, data scientists, platform engineers, and product teams.

Leads the design, administration, optimization, and modernization of enterprise data platforms across SQL Server, Azure, and Microsoft Fabric. Responsibilities include database operations, high availability and disaster recovery, performance tuning, replication, cloud migrations, monitoring, security, governance, and automation. The role collaborates with architects and infrastructure teams, mentors junior engineers, and serves as a subject matter expert for database infrastructure and cloud architecture.

Designs, builds, and maintains scalable ETL/ELT pipelines and finance data models using Snowflake, AWS, dbt Core, Python, and SQL. Develops Medallion architecture data marts, integrates ERP systems, and partners with stakeholders on financial workflows. Implements data governance, RBAC, automated quality testing, observability, documentation, CI/CD, and secure deployments. Monitors Snowflake costs and optimizes queries while supporting Oracle EBS integrations and the migration to Workday.

The Principal Data Engineer will lead data engineering efforts on public cloud, design scalable solutions, build data pipelines, and drive deliverables with a high level of technical expertise in relevant technologies.

Leads the architecture, development, modernization, and operation of enterprise data platforms and pipelines supporting credit risk, analytics, regulatory reporting, and AI initiatives. Builds scalable cloud lakehouse solutions using Databricks, Spark, Delta Lake, Python, and SQL; establishes governance, quality, lineage, observability, and compliance controls; optimizes performance and costs; leads technical initiatives; influences architecture; mentors engineers; and partners with product, analytics, science, engineering, and business stakeholders.

Leads the design and management of scalable cloud database infrastructure across AWS and Azure. Responsibilities include PostgreSQL and SQL Server administration, GraphQL and Hasura API development, cloud migrations, CI/CD automation, infrastructure as code, performance monitoring, security, compliance, data modeling, and mentoring engineers. The role also supports data access layers for AI and application workloads.

Leads architecture and hands-on development of scalable data pipelines, governed data models, semantic layers, analytics products, and AI workflows. Writes production SQL and Python, establishes engineering standards, mentors engineers, and drives design reviews. Builds agentic and LLM solutions using retrieval and managed AI services while ensuring data quality, observability, reliability, privacy, and regulatory compliance for PHI and genomic data. Partners with stakeholders, platform teams, and governance forums to deliver trustworthy business solutions.

Supports scalable ETL/ELT pipelines, cloud data platforms, database integration, machine learning model development and deployment, AI-enabled workflows, and analytics dashboards. Uses Python, data science libraries, AWS, GCP, Azure, SQL and NoSQL databases, Looker, Tableau, and automation tools. Collaborates with data engineers, analysts, data scientists, and marketing teams to deliver data-driven solutions. The role is hybrid in Dallas, requiring two in-office days weekly.

Conducts electronic warfare research, mission data development, validation, verification, and testing. Supports EW system programming and reprogramming, coordinates requirements with system engineers, prepares test plans, manages configuration control boards, and analyzes post-test data. The role requires domestic and international travel, U.S. citizenship, technical education or equivalent experience, systems design expertise, and eligibility for a Secret clearance.

Conducts electronic warfare research, mission data development, validation, verification, and testing. Supports EW system programming and reprogramming, develops test plans, participates in configuration control boards, and performs post-test data analysis. The role requires collaboration with systems engineers, U.S. citizenship, a technical degree or equivalent experience, Secret clearance eligibility, and 10–15% domestic and international travel.

Lead the design and implementation of enterprise data lake architecture for DoW projects. Administer Databricks environments, optimize Delta Lake and ETL pipelines, integrate AI/ML workflows, and develop secure cloud solutions across AWS, Azure, and GCP. Lead technical teams, establish DevSecOps and CI/CD practices, address architecture issues, and coordinate between clients, business analysts, and developers. The role requires TS/SCI clearance and expertise in cloud security, RBAC, compliance, and data governance.

Designs and develops complex cloud-native data solutions, scalable batch and real-time pipelines, APIs, data models, and enterprise data platforms. Implements data governance, quality, lineage, security, observability, CI/CD, automated testing, and operational reporting capabilities. Collaborates with architects, product owners, analysts, governance, security, and platform teams on technology strategy and cloud modernization. Mentors engineers, leads technical design discussions, presents recommendations, and supports AI-ready data products and emerging development tools.

Architect and operate enterprise-scale data platforms supporting cybersecurity operations. Build batch and streaming pipelines, data lakes, warehouses, storage backbones, normalization frameworks, schemas, and telemetry integrations. Optimize scalability, reliability, cost, security, and data quality while enabling SIEM/XDR analytics. Collaborate with security operations, detection engineering, threat management, and data science teams, and maintain architecture documentation and operational runbooks.

Design, build, deploy, and optimize scalable, high-performance data platform services and streaming infrastructure. Develop systems supporting analytics, machine learning, telemetry, and data products using distributed technologies. Collaborate with engineering and cross-functional teams, participate in design and code reviews, tune performance, debug production issues, and mentor engineers. The role requires backend software development, database expertise, API design, and distributed systems experience.

Architect and develop the spacecraft software platform for Blue Origin’s orbital data centers. Responsibilities include distributed workload orchestration, custom scheduling, container management, control-plane APIs, multi-tenancy, networking, storage, high availability, failure recovery, and resource optimization under power, thermal, and connectivity constraints. The role leads technical strategy, validation, testing, and cross-functional co-design with hardware and application teams.

Build and maintain production data ingestion, transformation, and pipeline systems for a governed federal data and AI platform. Develop data products, contribute to Terraform infrastructure and CI/CD, monitor quality and reliability, troubleshoot issues, and apply governance and access controls. Support data science and ML teams while operating within FedRAMP, NIST 800-171, and CUI requirements. Collaborate with engineers and document pipelines and processes.

Build enterprise data models, semantic layers, metadata, SQL transformations, and cloud data pipelines. Improve text-to-SQL and generative BI accuracy by partnering with AI engineers on RAG, agents, prompts, testing, and guardrails. Translate business needs into data requirements, validate KPIs, document schemas, apply quality and governance standards, and collaborate with technical and non-technical stakeholders across pharma, life sciences, manufacturing, and healthcare projects.

Designs and deploys data science pipelines, predictive models, retraining systems, and decision-support tools. Leads process improvement initiatives using Lean and Six Sigma, monitors KPIs, and manages operational data stewardship, governance, and integrity. Implements CI/CD, localizes data models, supports Management of Change and project commissioning, and collaborates with senior leadership on analytics strategy and budgets. The role involves office and industrial terminal environments, including chemical exposure, confined spaces, elevated work, and PPE requirements.

Owns the architecture and technical direction of Coalition’s enterprise data platform. Builds scalable Snowflake and dbt infrastructure, governed data models, batch and streaming pipelines, data products, semantic layers, observability, security controls, and AI/ML data foundations. Partners across engineering, product, underwriting, actuarial, security, and business teams while mentoring engineers and establishing technical standards.

Build and maintain secure data-ingestion pipelines, relational databases, ETL workflows, streaming integrations, and scalable data platform services. Support Kubernetes, Terraform, GitLab CI/CD, AWS, PostgreSQL, Kafka, and visualization applications. Improve performance, security, data lineage, and schema-aligned ingestion across structured and unstructured data. Mentor junior engineers and contribute to technical standards and operational improvements in secure enterprise environments.

Leads the design, development, deployment, and operation of scalable machine learning solutions and high-performance microservices. Builds real-time inference systems using Triton and TensorRT, guides distributed cloud architectures, participates hands-on in coding and reviews, and mentors engineers. Drives technical strategy, operational excellence, system resilience, and business-wide innovation across generative AI, graph machine learning, and big data initiatives.

Build, deploy, and own software applications, engineering-tool integrations, automations, data pipelines, and reporting systems. Manage AWS, Databricks, and GitLab CI/CD resources while ensuring reliability, security, and maintainability. Serve as product owner by gathering requirements, prioritizing roadmaps, communicating trade-offs, and supporting production tools. Collaborate with global engineering teams, process owners, and leadership.

The Staff Data Reliability Engineer ensures data integrity, quality, freshness, and availability across enterprise pipelines. Responsibilities include defining data SLOs, implementing observability, building resilient Informatica and Python/PySpark pipelines, automating validation and recovery, eliminating operational toil, leading data incidents and postmortems, creating runbooks, and applying AIOps, machine learning, and DataOps practices to detect anomalies and self-heal data workflows.

Own the organization’s data platform, including production pipelines, cloud warehouse architecture, dimensional data models, data quality, testing, documentation, and lineage. Partner with Finance, HR, and Operations stakeholders to build reliable reporting solutions. The role requires independent technical ownership, performance and cost optimization, schema management, and scalable integration of new data sources and business systems.