Top Data Engineer Jobs in Portugal

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.

Build and maintain workflow applications, relational data structures, data pipelines, SQL logic, REST API integrations, dashboards, and reporting outputs for a federal health client. Perform data validation, testing, documentation, code reviews, and release support while collaborating in an Agile environment. The role requires Python and SQL proficiency, strong technical fundamentals, and the ability to learn low-code platforms and federal delivery practices. Occasional travel to client locations may be required.

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.

Supports the Global Technology & Solutions team as a Data Engineer Intern. Assists with departmental projects, reports, operational and administrative tasks, and cross-functional initiatives under supervision. Builds understanding of supported businesses and customers while developing skills in data engineering, data warehousing, and big data. The role is in person in Mount Laurel, New Jersey, up to four days per week, with training and onboarding provided.

Leads Wasabi’s AI infrastructure, data factory, and developer partnerships strategy. Hunts and originates new relationships, develops joint go-to-market and co-selling motions, structures revenue-generating deals, and manages executive partner relationships. The role collaborates with Business Development, Sales, Marketing, and Product, builds the developer ecosystem, tracks pipeline and revenue performance, and represents Wasabi at industry events. Candidates need deep AI infrastructure and storage expertise, an established industry network, and a proven record of closing strategic partnerships.

Build and maintain high-throughput, low-latency distributed data systems across ingestion, storage, and retrieval. Optimize delivery pipelines, scale analytical database clusters (ClickHouse), develop GraphQL and log delivery solutions, monitor performance with observability tooling, troubleshoot production issues, and collaborate cross-functionally and with open-source communities to evolve the data platform.

Lead design and modernization of scalable, secure data systems and real-time pipelines for xOT cybersecurity. Create data contracts, observability, lineage, and cross-functional solutions supporting cloud and on-prem deployments, Kubernetes, and stream processing to enable threat detection and intelligence.

Develop, curate, and publish authoritative geospatial data products in ArcGIS Portal; build Python data-processing pipelines; and create Tableau dashboards and web maps. Manage metadata, hosted feature layers, and data discoverability while partnering with mission users to transform large spatial and tabular datasets into operational visualizations and automated workflows.

Lead design, develop, and maintain secure, scalable data and AI-driven systems. Build data pipelines on AWS/Databricks, write production code, drive AI-assisted engineering adoption, enforce validation/security standards, and mentor teams on responsible AI practices.

Build, scale, and monitor end-to-end data pipelines and infrastructure powering AI products; implement reporting solutions; optimize data models and queries; mentor teammates and support ML model productionization.

Designs, builds, deploys, and maintains scalable data architectures, databases, ETL/ELT pipelines, API integrations, and cloud data solutions in AWS GovCloud. Migrates on-premises databases to cloud platforms, optimizes data models and performance, implements governance and security controls, and supports reporting and analytics. Collaborates with architects, data scientists, and stakeholders while documenting systems and conducting integration, validation, cost, and risk assessments.

Designs, develops, and maintains scalable AWS data pipelines and infrastructure. Responsibilities include data ingestion, ETL transformation, storage, orchestration, monitoring, optimization, troubleshooting, data quality, governance, security, and privacy. The role partners with stakeholders to develop data solutions and supports reliable, accessible data for decision-making.

Lead engineering and deliver secure, scalable data and LLM-based systems for a Global KYC and Risk Assessment Data Platform. Define architecture, engineering standards, AI/LLM governance, and drive adoption of AI-assisted development practices while mentoring teams and ensuring operational stability and observability.

Design and deliver enterprise-scale data models and analytics-ready datasets in Snowflake; build reliable ELT/ETL pipelines (batch and near-real-time); enforce data quality, observability, and secure handling of PII; optimize performance and publish curated data to downstream systems while collaborating with product, operations, and engineering teams.