Top Data Engineer Jobs in Portugal

Designs, develops, and operates production data pipelines and curated, AI-ready data products for AbbVie R&D. Responsibilities include data integration, normalization, modeling, metadata, lineage, quality controls, governance, documentation, knowledge graph publication, and support for RAG and vector-search workflows. The role collaborates with scientists, engineers, architects, data owners, and vendors; provides technical guidance; monitors pipeline reliability; resolves issues; and ensures compliance with data security, GxP, and regulatory standards.

Designs, builds, deploys, and maintains scalable cloud-native data platforms and pipelines in AWS. Supports data acquisition, preparation, database architecture, cloud migration, AI/ML, data science, and business intelligence initiatives. Leads collaborative teams in evaluating data, designing solutions, and managing operational data flows. Requires TS/SCI clearance with polygraph and experience in all-source analysis and data engineering.

Designs, builds, deploys, and maintains scalable cloud-native data platforms and pipelines using AWS. Supports cloud migration, data science, AI/ML, and business intelligence initiatives by organizing structured and unstructured data. Responsibilities include database architecture, data acquisition and preparation, software development, platform modernization, analytics architecture evaluation, and cross-functional team leadership. Requires TS/SCI clearance with polygraph and substantial all-source analysis experience.

Designs and develops cloud data architectures, ingestion frameworks, data models, and analytical platforms. Advises clients on cloud migration, data warehousing, technical approaches, and implementation roadmaps. Collaborates with stakeholders through requirements gathering, workshops, whiteboarding, and project meetings while deploying end-to-end cloud analytics solutions. Requires experience with cloud platforms, big data technologies, BI tools, data warehousing methodologies, programming, SQL, version control, and Agile/DevOps practices.

Own the technical direction, architecture, roadmap, and operational health of Teamfight Tactics’ data ecosystem. Build scalable batch and near-real-time data systems, telemetry, schemas, data models, governance, observability, and reliable sources of truth. Lead cross-functional initiatives, define engineering standards, optimize costs, mentor practitioners, and partner with game, product, insights, and data science teams across feature development and live operations.

Participates in a 12-month new-graduate development program while designing, building, deploying, and operating data architectures and pipelines. Develops processes for capturing, transforming, storing, and maintaining structured and unstructured data across distributed and local systems. Applies automation, big-data, artificial intelligence, and machine-learning techniques to support data integrity, compatibility, and evolving business requirements.

Designs, builds, and optimizes cloud-native data platforms, pipelines, data models, and integration frameworks. Develops scalable ingestion and transformation solutions using PostgreSQL, Python, SQL, AWS services, and Apache Airflow. Establishes data engineering standards, monitoring, governance, observability, and automated recovery. Collaborates with business, architecture, analytics, and Agile teams to deliver secure, reliable data products and reporting capabilities.

Support senior engineers in building, testing, and maintaining enterprise data pipelines and integrations. Write and optimize SQL queries, implement data quality checks, logging, and documentation, and participate in source control, code reviews, sprint activities, and support response. Complete and present an assigned data engineering project while developing practical skills in databases, scripting, ETL/ELT, and cloud data engineering.

Own production data ingestion pipelines for healthcare and benefits data, including normalization, mapping, validation, reconciliation, observability, and secure handling of sensitive information. Unify census data processes, scale the platform, improve identity matching, and build automation and TypeScript tooling. The role requires strong Python and SQL skills, production orchestration experience, reliable testing and data-quality practices, and effective communication with technical and nontechnical stakeholders.

Develops and supports enterprise reporting, dashboards, data pipelines, and analytics solutions using SSRS, Tableau, OBIEE, SQL, and related technologies. Translates business requirements into governed reporting products, optimizes queries and data integrations, validates results, resolves production issues, and maintains documentation. Collaborates with data engineering, governance, security, business, and data science teams across cloud, on-premises, and hybrid environments while supporting data quality, compliance, and self-service analytics.

Develop and maintain Databricks notebooks, workflows, GitLab repositories, and foundational CI integrations. Troubleshoot access controls and Python library issues, integrate Databricks, GitLab, Jira, and Confluence APIs, and analyze project activity data. Establish merge request and code review standards, support DevSecOps practices, evaluate new technical tools, and provide technical guidance to combatant commands and cross-functional engineering teams. The role is onsite at Andrews AFB three days weekly and remote two days.

Build and maintain Databricks data pipelines, ETL/ELT processes, SQL transformations, and Power BI dashboards for HR and People Analytics. Ensure data quality, optimize models and reporting performance, troubleshoot issues, document solutions, and collaborate with HR stakeholders and technical teams to deliver scalable workforce analytics.

Designs scalable data solutions, builds and maintains large-scale data pipelines, models enterprise datasets, and enables analytics and AI/ML use cases using Palantir Foundry. The role involves developing Foundry applications and custom TypeScript frontends, integrating AWS data services and machine learning frameworks, optimizing distributed systems, and applying DevOps practices. Candidates should have data engineering, Python, TypeScript, cloud, big data, and Palantir Foundry experience; insurance industry experience and Palantir certification are preferred.

The Data Engineer III will design, implement, and optimize data services, ensuring they meet business needs and drive automation and best practices across Crunchyroll's data environments.

As a Principal Data Engineer, you will design and implement LLM, AI-powered security data platforms, mentor engineers, and drive the adoption of data solutions across teams.

The Data Engineer will design and build data solutions, develop ELT jobs, ensure data quality, and collaborate with technical teams to align data strategies with business goals.

Build and operate data-intensive web products and APIs, extend high-throughput ETL pipelines and scrapers, ensure reliability/observability, design self-scaling services, participate in code review, and collaborate across product, data, and infrastructure teams to meet production SLAs.

Leads data engineering initiatives by designing, developing, testing, implementing, and supporting cloud-based data solutions. Collaborates with Agile teams, product managers, and machine learning engineers; works with distributed systems, databases, streaming applications, and data warehouses. Performs code reviews and unit testing, optimizes performance, explores emerging technologies, and mentors engineering team members.

Build scalable, secure data pipelines, models, platforms, and governance systems that enable AI applications to access trusted enterprise data. Partner with analytics, marketing, and product teams to translate requirements into technical solutions, independently deliver projects through production, and support sensitive-data security and access controls. Preferred experience includes LLM architectures, cloud data platforms, infrastructure as code, regulated environments, and technical leadership.

Build and maintain recruiting analytics data infrastructure using BigQuery, dbt, Fivetran, SQL, and Python. Design dimensional models, ETL/ELT pipelines, semantic layers, governance controls, data security, monitoring, and standardized recruiting metrics. Integrate Workday, Greenhouse, and internal systems while supporting batch and real-time analytics. Partner with data scientists, engineers, recruiters, and business stakeholders to deliver trusted data products and AI-enabled workforce insights.

Build and operate production clinical-trial data pipelines using Python, PySpark, and SQL. Transform diverse source data into internal models, develop quality checks and reconciliation, investigate data issues, and improve observability. Partner with clinical subject matter experts and customer-facing teams to translate domain knowledge into reliable pipeline logic. Deliver customer-driven changes under enterprise SLAs while maintaining engineering standards through testing, code review, documentation, and CI/CD.

Design, develop, and maintain scalable data pipelines, models, databases, and data infrastructure using ETL and big-data technologies. Cleanse, integrate, and validate large datasets; implement data governance; troubleshoot complex data issues; and collaborate with analysts, scientists, and stakeholders. Develop and enhance software supporting mission requirements, maintain application databases, and coordinate hardware-software integration. The role requires onsite work in Arlington, Virginia, an active Secret clearance, and U.S. citizenship.

Design and scale lakehouse architecture, streaming and batch pipelines, and reliable data platforms using Kafka, Spark, Airflow, Iceberg, Databricks, and related technologies. Build Medallion-layer data systems, manage open table formats, optimize distributed queries, monitor platform reliability, resolve data quality issues, and collaborate with analysts, data scientists, and product teams.

Build and manage data ingestion, integration, transformation, modeling, and quality frameworks for federal financial management and IT programs. Analyze large datasets using SQL, Python, R, PySpark, and Databricks; develop predictive models and apply machine learning techniques. Ensure data governance and quality standards, define Databricks data quality metrics, support client meetings and documentation, and contribute to consulting and business development activities.

Architects, develops, and optimizes scalable data pipelines and cloud-native platforms integrating structured and unstructured data. Leads data engineering teams, establishes engineering standards, ensures data quality, and builds secure enterprise infrastructure supporting analytics, reporting, and mission-critical decision-making. Uses Python or Scala, SQL, Kafka, NiFi, AWS, and distributed systems while collaborating with multidisciplinary teams.