Top Data Engineer Jobs in Portugal

Build and maintain Databricks data pipelines across bronze, silver, and gold layers. Ingest APIs, logs, billing exports, and reference data; implement attribution logic, governance, data quality monitoring, and cost optimization. Manage Unity Catalog permissions, lineage, refresh schedules, incremental processing, and CI/CD workflows while supporting multi-cloud storage and high-volume caller-identity data.

Designs, implements, documents, administers, and optimizes data solutions, including relational and dimensional models, enterprise reporting models, secure data pipelines, and scalable analytics systems. Collaborates with stakeholders and uses Microsoft data technologies such as SQL Server, Azure Data Factory, Azure SQL, SSIS, SSAS, Stream Analytics, and Power BI Premium. The role requires regular on-site work at the Pullman, Washington headquarters.

Designs, implements, documents, and maintains enterprise data solutions, including relational and dimensional models, reporting models, data pipelines, and scalable data platforms. Ensures data accuracy, consistency, security, performance, and reliability while collaborating with stakeholders. Uses Microsoft data technologies including SQL Server, Azure services, SSIS, SSAS, and Power BI Premium. The role requires regular on-site work in Moscow, Idaho.

Build and maintain cloud-based data platforms, pipelines, data models, and analytics-ready datasets. Collaborate with stakeholders to translate requirements into technical solutions, manage medallion architecture layers, and ensure data quality, lineage, security, governance, and reliable operations. Monitor pipeline performance, troubleshoot failures, document remediation, and support enterprise data capabilities for reporting, analytics, data sharing, and AI-enabled use cases.

Design, build, and maintain large-scale GCP data pipelines and data warehouse solutions using Dataproc, GCS, BigQuery, Hadoop/Spark/Hive and Airflow. Mentor engineers, run agile ceremonies, prioritize backlog, provide on-call support, and deliver data models and products at multi-TB/PB scale.

The Data Engineer Intern will onboard and model datasets on cloud data platforms, build reliable ETL/ELT pipelines, and deliver high-quality data layers for analytics, reporting, and AI/ML workloads. Responsibilities include working with Databricks or Spark, cloud platforms, relational and NoSQL databases, data quality frameworks, version-controlled data models, Git, CI/CD, and modern development workflows. The intern will collaborate with AI, data, and finance professionals in a team environment.

Build and maintain large-scale data processing pipelines using Apache Spark (PySpark/Scala) and Python. Design ETL workflows, implement distributed data processing, use version control (Git), and work with cloud platforms (GCP/AWS/Azure).

Manage and support PostgreSQL and MySQL databases in a large, high-transaction environment. Own incidents and service requests, meet SLAs, review backups and monitoring reports, respond to alerts, troubleshoot performance issues, and maintain high availability, partitioning, security, and recovery processes. Improve database provisioning automation, monitoring, and alerting while collaborating with engineering teams on recurring issues. The role is hybrid in the Atlanta area.

Provide senior-level data engineering for analytics: resolve complex technical issues, build decision-support reports and dashboards, lead projects, prepare and integrate data, coach staff, and ensure high-quality analytics to support academic, research, and healthcare goals.

Design, build, and optimize GCP-based data architectures and ETL/ELT pipelines using BigQuery, Dataflow, dbt, and orchestration tools. Ensure data quality, security, and integration from diverse sources; collaborate with stakeholders to deliver actionable insights.

The Market Data Engineer will design and implement high-performance market data systems, focusing on trading infrastructure, performance optimization, and system resiliency.

Leads architecture and delivery of secure, scalable data platforms and pipelines on AWS and Databricks. Develops production code and reusable Python or Scala frameworks, ensures data quality and operational reliability, drives SDLC, CI/CD, testing, security, and resiliency standards, and mentors engineers. Partners across product, analytics, and security teams while promoting validated AI-assisted development practices and automating recurring operational remediation.

Build and maintain ETL/ELT pipelines, ingest and transform structured and unstructured data, integrate APIs and external sources, improve data quality and monitoring, support dashboards and ML pipelines, optimize warehouse and cloud performance, and contribute to data governance and documentation.

Design and build scalable batch and streaming data pipelines and a semantic data layer for connected vehicle telemetry. Collaborate with data owners and analytics stakeholders, implement cloud MLOps (GCP/AWS), ensure data quality and monitoring, integrate cloud AI tools, and mentor junior engineers.

Build and maintain batch and streaming data pipelines supporting enterprise analytics and AI. Develop transformations, analytical data models, quality checks, integrations, and reliable production workflows. Monitor and troubleshoot pipeline issues, support data-source onboarding, optimize platform performance and costs, and follow governance and security standards. Collaborate with BI developers, analysts, and data scientists while writing tested, documented code in an Agile environment.

Lead software engineering for cloud-based machine learning platforms. Design and maintain infrastructure supporting model development, training, deployment, serving, and monitoring on AWS. Automate testing, releases, and SDLC processes; monitor platform stability and resolve performance issues. Guide responsible use of AI-assisted development tools, contribute to architecture and design decisions, support platform automation, and coach engineers on secure, compliant engineering practices.

Design, build, and support data-centric integration services for portfolio construction and reporting, ensuring reliability and scalability across APIs and data pipelines.

Build and maintain Databricks-based data platforms, including ingestion, transformation, storage, governance, data modeling, and serving pipelines. Establish medallion architecture standards, canonical data models, quality controls, lineage, schema evolution, and reliable batch or incremental processing. Improve pipeline observability, scalability, idempotency, and recoverability while moving curated data to systems such as ClickHouse. Collaborate across application and analytics teams to create durable, governed production datasets.

Design, build, and maintain Keboola-based data pipelines integrating multiple business systems to deliver accurate, scalable data for supply chain, logistics, and transportation analytics; collaborate with analysts and engineers; support documentation, governance, and pipeline automation.

Architect, build, and operate secure, scalable data platform capabilities covering ingestion, transformation, orchestration, metadata, lineage, and data quality. Lead complex initiatives from architecture through production, establish reliability and engineering standards, automate deployment and onboarding, and drive operational excellence. Partner with security, risk, controls, and business teams on governance and regulatory requirements while mentoring engineers and influencing technical direction across teams.

Develops, tests, deploys, maintains, and debugs software solutions supporting data governance and automation. Designs backend and user interface applications, writes technical documentation, and applies Python, SQL, GenAI, Angular, React, and data analysis to build Data Governance Services. The role is based in an in-office environment and requires collaboration around customer needs and enterprise risk management.

Designs and optimizes scalable data pipelines and architectures supporting analytics and operational needs. The role develops ETL, streaming, data quality, governance, and cloud-based workflows using modern big data technologies. Responsibilities include translating business requirements into technical solutions, monitoring data systems, improving reliability and performance, and providing technical guidance on data modeling, storage, and metadata management.

Build and operate HomeLight’s data engineering systems, including scalable pipelines, ETL workflows, data warehouses, databases, schemas, and APIs. Analyze and visualize data, develop performance metrics, optimize data release processes, and provide reliable data for algorithms and internal teams. The role also involves applying LLMs and prompt engineering, designing service-oriented architectures, and building robust, highly available systems.

Designs, operates, and improves secure AWS cloud environments and ECS application platforms supporting data-driven applications. Builds and administers CI/CD pipelines using Jenkins and Spinnaker, improves reliability and scalability, supports Kafka and SQL dependencies, and partners with globally distributed Agile teams. The role also involves mentoring engineers, establishing reusable deployment standards, and driving operational excellence across Java/Spring Boot microservices and React micro frontends.

Leads software engineering for data platform and entitlement solutions within an agile financial services technology team. Designs, develops, troubleshoots, and maintains secure, scalable Java and Spring applications using microservices, Kafka, cloud platforms, and CI/CD. Creates architecture artifacts, develops relational data models, improves system architecture, and promotes responsible AI-assisted engineering practices. Preferred experience includes modern data platforms, Spark, IAM, authorization models, and enterprise AI integration.