Top Data Engineer Jobs in Portugal

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.

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.

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.

Build, maintain, and improve data integrations that extract, transform, and load retailer data into standardized schemas. Collaborate with engineering, Product, and Client Solutions teams to expand the data platform, improve reliability, monitor outages, and develop new integration capabilities. Work with data pipelines, cloud infrastructure, supply chain concepts, APIs, web scraping, browser automation, and EDI.

Build and ship production AI systems across retrieval, knowledge graphs, inference, agentic workflows, and data platforms. Develop customer-context retrieval, scored signals, evaluation and observability capabilities, and workflows that turn insights into action. Work hands-on with large and small models, durable orchestration, cloud deployment, inference-cost optimization, and high-volume observability data. Establish reusable engineering patterns, collaborate with product and platform teams, and mentor through strong technical contributions.

Build, deploy, and maintain scalable data pipelines and platforms for mission-driven projects. Validate and normalize datasets, develop schemas, implement ETL workflows, and create monitoring dashboards and metrics. Collaborate with AI/ML engineers, cloud architects, and technical leaders to support governed, high-quality data flows and reliable model performance. Improve pipeline performance and support data engineering for operational, research, and enterprise systems.

Build and maintain reliable data pipelines, integrations, automations, Airflow workflows, and dbt models. Develop semantic-layer datasets, monitor data quality, troubleshoot pipeline issues, document systems, and collaborate with business stakeholders. The role also involves applying AI, machine learning, and LLM-based tools to data engineering workflows, with potential Power BI and healthcare data work.

Designs and maintains scalable data infrastructure, Snowflake databases, data models, and automated ELT pipelines. Extracts, cleans, validates, and transforms data from databases, APIs, and files while ensuring data quality and integrity. Troubleshoots workflow failures, optimizes queries and pipelines, and collaborates with analysts, product owners, developers, and other engineers. The role may require after-hours ownership of critical data pipelines.

Designs and operates secure batch and streaming data pipelines across cloud platforms. Responsibilities include data ingestion, integration, modeling, quality validation, governance, lineage, security, monitoring, workflow orchestration, and performance optimization. The role supports analytics, machine learning, AI-enabled applications, and authoritative data services while documenting architectures, runbooks, and data contracts. Collaboration with engineering, cloud, security, data science, architecture, and customer teams is required, along with contributions to secure data platform proposals.

Build and maintain scalable data pipelines and platforms using SQL, Java, or Python. Acquire, tag, integrate, and process structured, semi-structured, and unstructured data. Apply machine learning models for classification, use BigID and AWS, and collaborate with analysts, engineers, developers, and data consumers in an agile environment. Guide analytical exploration, data examination, platform design, deployment, and maintenance for mission-driven client projects.

Lead the design, development, optimization, and monitoring of scalable cloud data pipelines using AWS Glue, PySpark, and Amazon Redshift. Integrate diverse data sources, ensure data quality, deploy infrastructure with Terraform, and improve reliability, performance, and cost efficiency. Collaborate with data professionals and stakeholders, document architecture standards, ensure security compliance, mentor junior engineers, conduct code reviews, and evaluate AWS technologies. The role requires a bachelor’s degree, substantial cloud data engineering experience, and hybrid work in specified locations.

Design and optimize Snowflake data warehouses, dbt transformation models, and scalable ETL/ELT pipelines. Develop ingestion utilities, improve SQL and warehouse performance, establish data quality and governance controls, and maintain CI/CD workflows using Git. Collaborate with analysts, data scientists, and business stakeholders across manufacturing, supply chain, and operations. The role also supports modern DataOps practices and may involve orchestration tools and cloud platforms.

Build and maintain scalable ETL/ELT pipelines using Azure Data Factory, Snowflake, dbt, and SQL. Develop data models, transformations, tests, and documentation; ingest data from APIs, databases, files, and enterprise applications; optimize performance; ensure data quality; troubleshoot failures; and support production deployments and monitoring.

Build and operate a security data platform connecting SIEM, identity, asset, threat intelligence, and knowledge graph systems. Design core APIs, distributed data pipelines, schemas, risk scoring, entity resolution, and coverage queries. Own architecture and platform workstreams from scoping through delivery, collaborate with detection and identity teams, review code, mentor engineers, and drive partner adoption. The role requires expert Python, deep Splunk expertise, cybersecurity experience, and senior-level project ownership.

Designs, builds, tests, maintains, and optimizes large-scale data pipelines, ETL processes, ingestion workflows, and cloud data storage. Develops Databricks data engineering and AI/ML pipelines, Azure Data Lake solutions, Power BI dashboards, and CI/CD automation. Ensures data quality, security, scalability, cost efficiency, and reusable engineering practices while collaborating with distributed technical teams.

Lead design, development, and operation of large-scale Spark-based data pipelines and lakehouse solutions. Write and review Python/PySpark and Spark SQL production code, optimize Spark workloads on EMR/Databricks, manage Apache Iceberg table operations, drive AI-assisted engineering adoption, automate reliability and CI/CD practices, and lead technical evaluations and communities of practice.