Top Data Engineer Jobs in Portugal

Build and maintain reusable data structures, automated Microsoft Fabric pipelines, SQL logic, and governed reporting foundations. Modernize SSRS reporting by centralizing duplicated logic, troubleshoot data-quality and pipeline issues, and support traditional and AI-assisted business insights. Partner with technical and business stakeholders to translate recurring reporting needs into scalable solutions while maintaining documentation and operational practices.

Designs, builds, maintains, and optimizes scalable data pipelines and ETL/ELT workflows using Azure Databricks, SAS, SSIS, SQL, Python, and Spark. Integrates data from internal and external sources, improves performance and reliability, troubleshoots complex processing issues, and supports cloud-based data platform architecture. Collaborates with engineering, analytics, product management, SRE, and other stakeholders to deliver high-quality data solutions.

Build and maintain automated ETL/ELT data pipelines, improve data quality and monitoring, optimize Postgres and BigQuery schemas and queries, develop internal tooling, support data governance (HIPAA), participate in on-call rotation, and collaborate across engineering teams to scale data systems.

Build and maintain scalable Databricks and PySpark data pipelines using SAP S/4HANA and ECC data. Develop governed bronze, silver, and gold datasets across enterprise domains, implement data quality and observability controls, and translate SAP processes into reusable semantic data products. Manage Unity Catalog and Immuta governance, access policies, lineage, and auditing. Collaborate with technical and business teams, advise other engineers, and establish reusable patterns, documentation, and production-ready engineering practices.

Leads customer-facing enterprise data engineering engagements for federal clients, designing scalable data architectures, pipelines, integrations, APIs, object storage solutions, and Java-based applications. Responsibilities include architecture workshops, technical implementation, troubleshooting, documentation, customer communication, mentoring junior engineers, and service innovation. The role requires active Top Secret clearance, at least eight years of relevant experience, strong consulting skills, and expertise across enterprise software, data engineering, and integration technologies.

Design, build, and maintain scalable big data pipelines, architectures, distributed databases, and storage systems. Use Python and frameworks such as Hadoop, Spark, or Flink for data integration, transformation, and processing. Ensure system performance, availability, security, governance, and data quality while collaborating with cross-functional teams and documenting technical solutions.

Designs, builds, and maintains enterprise data pipelines, ETL processes, integrations, cloud data infrastructure, and complex data models. Supports analytics partners, develops data warehouse solutions, tunes SQL and database performance, troubleshoots production issues, and delivers end-to-end data engineering projects. The role collaborates with architects, analysts, developers, and business stakeholders, applies big data technologies, supports agile delivery, and mentors team members.

Lead migration of financial data pipelines from Snowflake to AWS, design SOX-compliant dimensional models, build Medallion Architecture datasets, implement data quality, observability, CI/CD, and performance optimizations, and partner with stakeholders to deliver production-ready financial analytics at scale.

Leads architecture, administration, security, performance, availability, and lifecycle management for mission-critical on-premises Oracle databases supporting GEOINT dissemination. Administers spatial data stores, executes upgrades and patching, implements STIGs and encryption, automates operational tasks, and performs performance tuning. Leads incident response, cross-domain support, documentation, and compliance activities while mentoring database staff and briefing government customers and senior leadership.

Build and operate a governed Snowflake enterprise data warehouse, including ingestion, orchestration, dbt transformations, infrastructure as code, security controls, reliability checks, and CI/CD. Partner with data owners and BI teams during migration from legacy systems, ensure compliance for sensitive financial data, support reconciliation and incident resolution, and mentor engineers. The role requires hands-on ownership of pipelines and platform components across development and production environments.

Build and operate production-grade data pipelines and products for manufacturing customers. Lead discovery, architecture, implementation, testing, deployment, documentation, and operational handoff. Model manufacturing data, ensure data quality and observability, and create reusable pipelines and analytics products. Work directly with customer data, manufacturing, IT, product, and engineering teams to solve ambiguous operational problems and deliver reliable solutions.

Lead the architecture and technical direction of Curaleaf’s modern data and analytics ecosystem. Design scalable Snowflake platforms, data models, ingestion and transformation pipelines, governance practices, CI/CD workflows, and reusable data products. Partner across engineering, analytics, data science, governance, and business teams; mentor engineers; optimize performance, security, cost, reliability, and workload management; and communicate technical tradeoffs to executives and stakeholders.

Provides data engineering and analysis consulting for a Department of Defense client. Responsibilities include data modeling, data integration, software development, collaboration with clients and technical teams, and delivery of data solutions. The role requires an active Secret clearance, a bachelor’s degree, and at least three years of relevant experience. Preferred qualifications include Palantir, visualization, big data, programming, ETL, consulting, and Agile experience.

Designs, builds, and maintains data infrastructure, ETL pipelines, data models, and analytics data warehouses. Ensures accurate data flow from creation through presentation layers and supports reporting, analytics, and machine learning initiatives. Collaborates with stakeholders to deliver data products, improves engineering tooling and pipelines, participates in on-call support, and uses cloud, containerization, orchestration, streaming, and CI/CD technologies.

Leads the design and development of data engineering and AI-enabled solutions for legal, client, and internal business applications. Builds Python and SQL workflows for structured and unstructured data, integrates generative AI and APIs, validates AI outputs, establishes human-in-the-loop quality controls, and supports responsible AI governance. Collaborates with attorneys and technical teams, evaluates emerging technologies, prototypes self-service tools, and provides technical guidance to less experienced professionals.

Leads the development of customer-focused data and analytics solutions, including data models, reports, dashboards, and self-service tools. Partners with stakeholders and data product owners to improve data quality, governance, accessibility, and analytics adoption. Provides technical leadership, mentors team members, manages project deliverables, and supports scalable data products. Uses SQL, Python, visualization platforms, and other analytics tools to transform data and deliver actionable business insights.

Lead architecture and strategic direction for Pinterest's data warehouse, analytics tools, and data governance. Drive cross-functional initiatives to build scalable data platforms, AI-assisted pipeline tooling, and analytics capabilities. Mentor engineers, define policies and tooling, and deliver measurable adoption and business impact across the company.

Design and lead development of TrendAI’s large-scale data platform, including ingestion, CDC, replication, transformation, governance, cataloging, semantic discovery, and analytics capabilities. Enable governed self-service dbt across OLTP systems, build distributed SQL and streaming architectures, establish data quality and lineage standards, and create reusable platform APIs. Provide technical leadership, mentor engineers, and guide cross-team architecture for a security-focused, multi-product data platform supporting analytics and AI applications.

Leads technical direction for end-to-end machine learning and data systems powering digital products. Builds feature pipelines, predictive and recommendation models, training and inference workflows, APIs, and MLOps capabilities. Owns production deployment, monitoring, retraining, reliability, governance, and responsible AI practices while partnering with Product, Design, Data Science, and Engineering. Mentors engineers and establishes reusable standards for scalable ML and data platforms.

Designs, administers, supports, and optimizes Azure data platforms, initially focusing on Azure Databricks and Lakehouse environments. Responsibilities include cluster and workload management, performance tuning, data integration, security and governance, monitoring, troubleshooting, cost optimization, and platform automation using Infrastructure as Code and CI/CD. The engineer supports Azure data services, assists application teams, participates in cloud modernization initiatives, and provides on-call operational support.

Researches, designs, implements, and deploys machine learning algorithms for enterprise applications and federal customers. Collaborates with data scientists, software engineers, and subject matter experts to develop analytics capabilities, support application development, and deliver scalable solutions. Requires strong Python programming, applied machine learning, mathematical expertise, communication skills, and the ability to manage concurrent projects independently and collaboratively. Cloud, deep learning, NLP, computer vision, and reinforcement learning experience are desirable.

Architect and build terabyte-scale robotics data pipelines from fleet sensor streams through training ingestion. Curate, preprocess, annotate, version, and validate datasets; analyze data coverage, drift, redundancy, and quality; mine scenarios using embeddings and models; and develop samplers, benchmarks, and evaluation datasets. Partner with ML researchers to diagnose model failures and create targeted data mixtures and processing workflows that improve robot-learning performance.

Leads the design, development, and optimization of scalable batch and streaming data pipelines for drilling, equipment, and operational analytics. Builds data models and features supporting reporting, machine learning, AI, predictive maintenance, and drilling optimization. Ensures data quality, testing, monitoring, observability, and CI/CD reliability while providing technical leadership, mentoring, and cross-functional collaboration with engineering, data science, and operations teams.

Designs and builds scalable microservices, distributed applications, ETL pipelines, and AI data workflows for autonomous vehicle data. Responsibilities include streaming, orchestration, security, data management, model deployment, RAG workflows, video curation, behavioral search, and optimization of large-scale distributed systems. The role collaborates with software and AI experts while using technologies such as Python or Golang, Kubernetes, Hive, Parquet, SQL, vector databases, Spark, and NVIDIA RAPIDS.

Develop and maintain scalable data platforms, pipelines, ETL and ELT workflows for structured and unstructured data. Build software and scripts to retrieve, parse, process, and convert data for reporting and analytics. Collaborate with analysts, developers, and data consumers in agile teams while supporting platform monitoring, scaling, and operations for mission-driven client projects.