Top Data Engineer Jobs in Portugal

Design and modify data solutions using Databricks and Palantir Foundry; build data models and reports from ERP and legacy-system datasets; define and execute tests; and translate stakeholder reporting needs into technical specifications. The role requires SQL, Python, PySpark, database architecture, cloud architecture, software engineering practices, independent execution, documentation, and collaboration. It supports an Army client in a hybrid Arlington, Virginia environment and requires an active Secret clearance.

Build the core visualization platform, reusable frameworks, design systems, and developer tooling for client-facing financial analytics. Translate complex market and trading data into clear, high-performance applications while partnering with product, design, sales, and engineering teams. Evaluate emerging technologies, provide hands-on technical leadership, conduct design reviews, and mentor engineers. The role requires strong software engineering, visual design judgment, interaction design, and information architecture expertise.

Leads enterprise data platform architecture, engineering standards, automation, security, governance, observability, and cloud integration. Designs scalable batch and streaming pipelines connecting SAP, legacy systems, Databricks, and enterprise data products. Guides SAP transformation data harmonization, analytics and reporting strategy, infrastructure as code, CI/CD, reliability, and platform roadmaps. Provides hands-on technical leadership, mentors senior engineers, collaborates across business and transformation teams, and supports critical incidents.

As the first in-house Data Engineer, you will own the data infrastructure, build and maintain data pipelines, and collaborate with teams to ensure quality data access and insights.

Creates and manages data connections, validates and transforms client files, integrates external APIs and FTP feeds, and normalizes portfolio data into Clearwater’s standardized format. Monitors and automates daily processes, improves auto-reconciliation, resolves data issues, and implements operational enhancements. Communicates technical workflows to stakeholders, documents procedures, and provides light on-call support.

Lead the design, development, testing, and optimization of large-scale data pipelines and cloud-native data platforms. Build Python and PySpark applications, manage ETL workflows, Snowflake environments, data lakes, and AWS services, and promote secure engineering, CI/CD, automated testing, and responsible AI-assisted development. Mentor engineers, participate in code reviews, and lead technical delivery initiatives.

Designs and builds high-performance data infrastructure for machine learning model training and feature serving. Responsibilities include developing distributed processing architectures, optimizing training data pipelines, resolving performance bottlenecks, collaborating with ML research teams, and establishing tooling and best practices for ML data infrastructure. The role requires experience with fault-tolerant distributed systems and frameworks such as Apache Spark or Flink.

Design and build PerfectServe’s unified cloud data platform, including ingestion pipelines, integrations, data models, metrics, and consumer-facing data layers. Establish architecture, engineering, quality, and testing standards; enable safe extraction from production systems; partner with data users; and mentor data engineers. The role requires substantial cloud data platform experience, deep SQL, warehouse expertise, ELT, change-data-capture, AWS, modeling, orchestration, and data quality skills.

Build and operate production data pipelines that ingest, transform, validate, and deliver messy clinical data for machine learning, analytics, and clinician-facing APIs. Own data contracts, monitoring, backfills, failure recovery, and troubleshooting across systems. Collaborate with integration, ML, and product engineering teams to ensure reliable data delivery across hospital deployments.

Designs, develops, and maintains large-scale data processing systems, including ETL processes, data models, scalable pipelines, databases, data warehouses, and cloud-based big data solutions. Uses Snowflake, Fivetran, dbt, and Airflow while applying DevOps and security practices. Optimizes distributed processing and storage systems, collaborates with data scientists and analysts, and mentors junior engineers.

Leads the design, development, and maintenance of scalable AWS data platforms, pipelines, ETL/ELT workflows, data models, and orchestration systems. Uses Python, PySpark, SQL, dbt, AWS services, modern lakehouse technologies, and CI/CD practices to deliver secure federal data solutions. Responsibilities include platform modernization, performance optimization, AI-enabled data integration, compliance support, technical mentoring, architecture discussions, and cross-functional Agile collaboration.

Lead the technical direction and evolution of Noetica’s data platform, architecting scalable pipelines and storage systems for high-volume legal and financial data. The role owns platform strategy, governance, privacy, compliance, reliability, and security; partners with AI and machine learning teams; mentors engineers; guides architecture decisions; and remains hands-on building critical production systems.

Designs, implements, and operates secure, scalable data infrastructure and high-throughput AWS pipelines for Venmo. Builds streaming solutions using Kafka and AWS SQS, automates infrastructure provisioning and monitoring, develops data pipelines and tools, manages relational and NoSQL databases, troubleshoots large-scale performance issues, and collaborates with product, engineering, analytics, and executive stakeholders.

Build and operate Grassroots Carbon’s unified data platform, including AWS-based storage, production ingestion pipelines, scheduled datasets, integrations, model deployment, monitoring, security, and access controls. Migrate and reconcile historical PastureMap data while establishing lineage, shared identifiers, validation, and reproducible workflows. Partner with science, product, backend engineering, and operations teams to productionize models and deliver reliable data services supporting soil carbon modeling, grazing management, and rancher-facing products.

Lead design and implementation of Pinterest's exabyte-scale data lake storage and metadata systems. Drive architecture and execution for scalable storage features supporting large ML/AI workloads, collaborate across data/ML/infrastructure teams, and contribute to open-source projects (Iceberg, Spark, Flink).

Staff Data Engineer responsible for defining data architecture strategy, building fault-tolerant batch and real-time streaming infrastructure, and managing scalable lakehouse and database systems. The role processes telemetry from millions of connected devices, drives performance tuning and data quality, supports on-call operations, evaluates modern technologies, and mentors engineers. It requires strong expertise in Python, SQL, cloud data platforms, streaming systems, infrastructure as code, and enterprise-scale architecture.

Drive architecture and standards for scalable data infrastructure, pipelines, and data management. Build cross-functional data models and reliable batch and streaming workflows, including ingestion of financial and transactional data. Use SQL, Python, cloud data platforms, orchestration tools, and transformation frameworks to ensure data quality, monitoring, compliance, and actionable insights. Lead initiatives, collaborate with stakeholders, and mentor engineers.

Build and operate scalable data infrastructure, including fault-tolerant pipelines, data discovery services, and data lineage systems. Develop backend services that process petabytes of data for machine learning and autonomy teams. Lead system design, code reviews, and technical contributions while using AWS cloud ETL technologies and distributed systems. Collaborate with stakeholders to improve platform adoption and address data customer needs.

Build and own cloud-based data pipelines and governed data products supporting Employee Benefits Sales and Underwriting. Design ELT solutions, integrate complex data, improve data quality, and implement CI/CD and enterprise data engineering practices. Architect hybrid cloud solutions using AWS, Hadoop, Spark, Kafka, Snowflake, and related technologies. Influence architecture and tooling decisions, document technical guidance, mentor teams, evaluate emerging technologies, and contribute to strategic roadmaps.

Lead architecture and delivery of enterprise-scale data platforms, pipelines, and MDM. Integrate agentic AI/LLM workflows, build ML-ready data foundations, set engineering standards, lead cross-team technical initiatives, and mentor senior engineers.

Designs, builds, and maintains batch and near-real-time data pipelines and curated datasets for insurance and annuity reporting, analytics, and business operations. Translates business requirements into integration solutions using SQL and ETL tools, implements data quality and reconciliation controls, optimizes workflows, documents lineage, and supports testing, deployment, monitoring, and issue resolution. Works independently on moderately complex pipelines while escalating architectural decisions to senior engineers or data architects.

Develop and maintain Power BI dashboards, data visualizations, analytics solutions, and reporting tools for DHS clients. Lead data cleaning and transformation using Python, support data-driven decision-making, gather requirements, present technical findings to nontechnical audiences, and contribute to business development, proposals, internal initiatives, and professional development.

Develops and maintains Power BI dashboards, data visualizations, analytics solutions, and reporting tools for DHS decision-making. Leads data-cleaning scripts using R, supports ad hoc analytical requests, gathers requirements, presents technical findings to clients, and contributes to business development and internal initiatives. The role also involves data integration, UI/UX for dashboards, and client-focused consulting.

Design, build, and optimize scalable AWS and Snowflake data pipelines, databases, data models, and cloud-based solutions. Implement ETL/ELT workflows, data quality and monitoring frameworks, CI/CD pipelines, automated recovery, documentation, and performance improvements. Collaborate with stakeholders and cross-functional teams in an Agile environment while conducting advanced analysis using Python and SQL.

Analyze, clean, validate, and transform data from multiple sources; build Kibana dashboards and visualizations; maintain data pipelines and self-service analytics assets; document data models and workflows; and translate findings into actionable recommendations for technical and non-technical stakeholders.