Top Data Engineer Jobs in Portugal

Leads the design, development, and implementation of scalable data pipelines, APIs, cloud-based integrations, and Customer Data Platform solutions. Builds unified customer data ecosystems supporting analytics, personalization, and enterprise integration. Responsibilities include data modeling, Lakehouse and API optimization, governance, privacy compliance, CI/CD automation, monitoring, troubleshooting, documentation, and cross-functional collaboration. Requires extensive experience with Python, SQL, REST APIs, cloud platforms, data integration tools, CDPs, and modern data engineering architectures.

Design, build, and maintain software applications; collaborate with engineers, analysts, and stakeholders; resolve bugs and production issues; write scalable, testable code; perform testing and debugging; produce technical documentation; mentor peers; participate in on-call rotation; leverage the team's data ecosystem to inform application design and improvements.

Build and scale analytical data products, models, semantic layers, and reusable metrics using dbt and Databricks. Develop Gold-layer and dimensional models, support Tableau analytics, automate data workflows with Python and SQL, and implement testing, documentation, lineage, Git, CI/CD, and deployment practices. Provide technical leadership, architecture guidance, mentorship, code reviews, performance optimization, and production support while partnering with business, product, analytics, and engineering teams.

The Senior Data Engineer designs and maintains scalable data pipelines, ETL/ELT processes, enterprise data warehouses, and data models supporting clinical and operational analytics. Responsibilities include data integration using HL7, FHIR, APIs, and SFTP; data quality, governance, lineage, and compliance; performance optimization; issue resolution; documentation; and collaboration with analysts, clinical leaders, and business stakeholders. The role also provides technical mentorship and ensures reliable, secure healthcare data exchange.

Own NimbleRx’s data platform end to end, including ingestion, transformation, storage, querying, access, batch and streaming pipelines, warehouse modeling, security, compliance, and reliability. Optimize PostgreSQL and Java/Spring data-layer performance, investigate infrastructure issues, develop AI-enabled tooling, partner across teams, and mentor engineers and analysts.

Design, develop, and maintain data pipelines and software solutions to process and combine diverse data sources. Perform statistical analysis (regression, anomaly detection, clustering), automate and optimize data products, communicate findings to technical and non-technical stakeholders, elicit system requirements, validate performance, and support time-sensitive intelligence operations.

Lead the architecture and development of Vercel’s Finance Data Platform supporting revenue recognition, consumption-based billing, forecasting, reporting, and compliance. Build reliable batch and real-time pipelines using Kafka, ClickHouse, Tinybird, and Snowflake; establish governance, lineage, auditability, monitoring, and fault tolerance; translate Finance and Accounting requirements into scalable systems; guide architecture and roadmap decisions; collaborate with Security, Legal, Compliance, Audit, FP&A, and RevOps; and mentor data engineers.

Lead the architecture, development, and optimization of Snowflake and enterprise data platforms. Design scalable ETL/ELT pipelines, dimensional data models, and medallion architectures while establishing engineering standards for testing, deployment, monitoring, security, and data quality. Partner with stakeholders on technical roadmaps, mentor engineers and analysts, conduct design and code reviews, modernize legacy pipelines, and deliver reliable, reusable data solutions.

Own and evolve the enterprise data platform by architecting scalable data models, integrations, warehouses, and pipelines. Build and maintain solutions using Snowflake, dbt, Fivetran, Airflow, AWS, and Power BI. Integrate diverse data sources, implement monitoring and data quality frameworks, troubleshoot production issues, and deliver curated datasets for analytics and AI use cases. Provide technical leadership, establish engineering standards, and collaborate with business, governance, QA, and analytics teams.

Build and optimize batch and real-time ETL/ELT pipelines, data lakes, and cloud-based processing systems. Perform data ingestion, transformation, profiling, validation, governance, quality checks, and API integration across AWS and Azure environments. Write Python, JavaScript, and SQL; maintain technical documentation; support reporting and machine learning; mentor junior engineers; and ensure secure, reliable, compliant data operations.

Lead data engineering activities by designing, building, deploying, and maintaining scalable data platforms and pipelines. Mentor multidisciplinary teams, oversee analytical data examination, guide enterprise software development, establish execution priorities, and support complex distributed applications in agile environments. The role requires Java, Spring, Linux, containers, CI/CD, and a TS/SCI clearance with polygraph.

Designs, implements, and supports secure research software, data workflows, and cyberinfrastructure. Partners with researchers across disciplines to identify solutions, adapt workflows to cloud environments, and improve productivity, reliability, and reproducibility. Develops automation for cloud account management, contributes to software planning and project tracking, provides technical expertise, and delivers training workshops. May support operational emergencies, nonstandard shifts, and occasional equipment installation or troubleshooting.

Build and operate large-scale data pipelines, web-scraping systems, and data-collection tools for AI datasets. Maintain analytical databases, optimize queries, troubleshoot pipeline and data-quality issues, manage containerized workloads across Kubernetes and Linux infrastructure, and support CI/CD workflows. The role also involves documenting technical processes, researching improvements, and developing scalable APIs in a fully remote environment.

Build and maintain scalable computer vision data pipelines for images, videos, and metadata. Develop ETL workflows using NiFi and Prefect, media-processing services with Java, FFmpeg, and OpenCV, and containerized services with Docker. Collaborate with computer vision, MLOps, and platform teams while optimizing performance, monitoring reliability, testing data quality, and supporting high-volume media processing.

Design, build, and iterate data engineering pipelines, dashboards, and reports to support analytics and data science. Partner with stakeholders to translate requirements into engineering solutions, promote self‑service analytics, define best practices, and support implementation using Databricks, Snowflake, and Azure components.

Own and build the company’s data warehouse architecture, curated and semantic layers, data models, quality standards, and migration automation. Develop Redshift-based pipelines using Python, PostgreSQL, Fivetran, dbt, AWS, Airflow, and Terraform. Establish metrics governance, observability, infrastructure-as-code, AI-enabled enrichment, anomaly detection, metadata generation, and structured clinical-record ingestion. Partner with engineering, product, and commercial teams to define metrics and reliable platform change contracts while mentoring data engineers.

Lead the architecture and technical direction of a cloud data platform spanning infrastructure, Databricks, CDC streaming, pipelines, data products, developer experience, and governance. Resolve cross-team technical challenges, establish platform standards, influence roadmaps, drive adoption, mentor senior engineers, and lead complex multi-squad initiatives with minimal direction.

Develop and optimize data pipelines, transformations, feature stores, graph and relational data systems, and model-serving infrastructure supporting AI and ML applications. Collaborate with platform and engineering teams, implement data quality and governance practices, monitor infrastructure, troubleshoot issues, document processes, and mentor junior engineers. The role includes operational support, on-call participation, security compliance, and work with cloud, ML lifecycle, Kubernetes, vector database, and observability technologies.

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.

Develop and optimize Oracle Fusion Data Intelligence analytics, reporting, semantic models, data integrations, and augmentation solutions. The role supports Oracle ERP, HCM, and SCM data, including OTBI, BI Publisher, ODI, ETL, SQL, and PL/SQL development. Responsibilities include requirements gathering, data cleansing and validation, cross-functional collaboration, and contributing to an ERP analytics roadmap. This is an onsite role in Des Moines, Las Vegas, or Portland, with an initial 3–4 month W2 engagement.

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.

Designs and optimizes scalable data pipelines, Snowflake data warehouse solutions, and dbt transformation frameworks. Develops dimensional and enterprise data models, ETL/ELT processes, quality tests, documentation, and governance practices. Tunes Snowflake performance and costs, supports migrations and integrations, and collaborates with stakeholders, analysts, architects, and engineers to deliver analytics and reporting solutions.

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.