Top Data Engineer Jobs in Portugal

Build and operate the Safeguards data platform: ingestion and processing pipelines, data stores, governance, retention and access controls, and portable systems across cloud providers. Maintain high operational standards, ensure data integrity and privacy, and partner with analysts, investigators, and researchers to provide tooling and reliable data for detection, review, and mitigation systems.

Develop and optimize multilingual machine translation and speech models for low-resource languages. Build and manage multilingual datasets, scalable data pipelines, and end-to-end MLOps workflows. Deploy cloud-native machine learning solutions on AWS using containerized environments and CI/CD practices. Conduct model testing, benchmarking, monitoring, troubleshooting, and lifecycle management while researching emerging NLP, speech, generative AI, and machine learning technologies.

Designs and operates scalable data pipelines and platforms for analytics, machine learning, and Agentic AI. Develops production-grade Python and SQL solutions, implements data quality and monitoring, supports end-to-end ETL/ELT workflows, and partners with data scientists, analysts, and business stakeholders. The role also includes code reviews, testing, source control, production support, mentoring, and establishing engineering best practices. Familiarity with Celonis, SAP, and Salesforce is preferred.

Owns the architecture and development of pricing data pipelines, models, platforms, and automation. The role supports price waterfall analysis, discount optimization, segmentation, margin analytics, data quality, near-real-time insights, and machine learning use cases. Responsibilities include production Python development, SQL-based ETL/ELT, cloud pipeline optimization, testing, monitoring, source control, technical mentoring, and collaboration with pricing, commercial, and technical stakeholders.

Own the enterprise data layer on SAP HANA Cloud, designing relational and graph models, optimizing SQL and SQLScript performance, and building ingestion and replication pipelines. Develop secure service layers, enforce schema and database standards, implement multi-tenant access controls, and manage monitoring, recovery, capacity, and upgrades. Partner with machine learning and search teams to support feature pipelines and indexing, while documenting data models and graph schemas.

The Senior Data Engineer designs and maintains production data pipelines on GCP using BigQuery, Dataform, Airflow, Dataflow, and Cloud Run. They own data validation, cleansing, troubleshooting, API integrations, and reliable datasets for analytics and AI/ML. The role also involves translating client requirements into data solutions, maintaining dashboards, supporting ad hoc requests, and applying Git, code reviews, and CI/CD practices.

Own production LLM-agent systems for business data enrichment, including industry classification, web discovery and verification, person-to-business linking, risk scoring, shared agent infrastructure, tools, evaluation, observability, model selection, prompt engineering, and cost and latency optimization.

The Software Engineer will manage data collection, optimize cloud infrastructure for data ingestion, and collaborate with AI researchers to enhance datasets for model training.

The Software Engineer will manage data collection, operate cloud infrastructure, and collaborate with scientists to enhance AI models at Speechify.

Build and operate secure, production-grade batch and streaming data pipelines supporting valuation and monitoring. Own ingestion, change data capture, transformation, reconciliation, quality controls, dimensional modeling, client delivery, CI/CD, testing, deployment, monitoring, and incident response across Azure Databricks, Snowflake, SQL Server, and related platforms. Collaborate with product, valuation, client-facing, and technical teams to develop metadata-driven, multi-tenant data solutions.

Build and operate reliable ETL and ELT pipelines across lakehouse and transactional systems. Develop data models, enforce governance, integrate diverse data sources, and support analytics, APIs, and predictive tools. Monitor performance, data quality, lineage, cost, and uptime while collaborating with data science, AI, product, and innovation teams.

Supports the Fraud Data Engineering team through data integration, quality validation, analytics, documentation, and platform modernization. Responsibilities include gathering requirements, creating business requirement documents and source-to-target mappings, enabling batch and real-time integrations, analyzing large datasets, documenting data flows, participating in Agile activities, and developing Databricks notebooks and validation frameworks. The role is a 12-week internship focused on fraud and security data solutions.

Supports enterprise data engineering initiatives involving real-time event streaming, Voice of the Member analytics, master data management, data quality, metadata, and AI technology proofs of concept. The intern analyzes data flows and feedback, assists with platform governance and automation, researches technology trends, collaborates with engineers and stakeholders, and presents findings. The 12-week program requires current enrollment in a technical degree program with graduation in December 2027 or later.

Designs and supports testing of industrial screw pumps for defense applications. Collects, databases, visualizes, and analyzes CMM and pump test data; develops regression and predictive models; automates reporting; documents nonconformances; and creates dashboards for stakeholders. Collaborates with design, manufacturing, quality, sales, and program teams to improve product robustness, testing, engineering standards, and manufacturing processes. Provides customer support and contributes to technical documentation and process improvements.

Own and maintain dbt data models across design, testing, deployment, monitoring, and documentation. Translate ambiguous Marketing and Product needs into reliable data solutions, write design documents, support production issues, create runbooks and schema documentation, and coach junior engineers. The role uses SQL, Python, Snowflake, dbt, Dagster, and AWS while partnering closely with Marketing, Data, and MarTech teams.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, LTV, and next-best-action decisioning. Design reliable signal contracts covering freshness, provenance, confidence, eligibility, and calibration. Lead feature pipelines, model serving, experimentation, monitoring, and feedback loops while evaluating fairness, risk, compliance, trust, and long-term customer impact. Collaborate across product, growth, data, platform, modeling, risk, and compliance teams.

Designs, deploys, and manages cloud infrastructure using OpenStack and comparable platforms. Supports Linux and Python-based software engineering, IT management, system engineering, network automation, cloud security, and operations. The role requires substantial software engineering experience, strong communication skills, and an active TS/SCI clearance with polygraph.

Manage data requirement and electrical change requests for the U.S. Navy Strategic Weapon System. Coordinate with contractors, lead pre-coordination meetings, develop and drive change requests, perform impact reviews on SPALT and contractor documentation, support technical resolution with SSP working groups, and implement changes into SSP documentation. Limited off-site travel may be required.

Build and operate production machine learning systems for ranking, retrieval, recommendations, search, propensity, churn, lifecycle intelligence, and next-best-action decisioning. Design reliable signal contracts with freshness, provenance, confidence, and calibration guarantees. Lead experimentation, monitoring, feedback loops, and impact evaluation focused on fairness, trust, risk, compliance, and long-term engagement. Partner across product, growth, data, platform, modeling, risk, and compliance teams.

Analyze data using SQL and relational database tools; design tables, views, procedures, and database objects; support reporting; identify trends, anomalies, and outliers; organize information from multiple sources; and validate databases, program results, and reports for accuracy and completeness.

Build and govern Anthropic’s quote-to-cash data foundation. Design canonical models from Salesforce, CPQ, and billing data; establish data integrity standards and SLAs; collaborate with systems engineers on schema changes and ingestion; and create data products, dashboards, and self-service analytics tools for GTM and Finance teams. The role also owns data architecture, translates stakeholder needs into technical requirements, and influences roadmaps.

Design, build, and operate scalable data infrastructure for LLM research, including distributed compute, ingestion, processing, storage, data catalogs, deduplication, quality controls, monitoring, and search. Collaborate with researchers to improve data quality, accelerate training, and develop reliable, reproducible systems across petabyte-scale multimodal datasets.

Owns the end-to-end autonomy data platform, including recovering and uploading flight data, processing synchronized sensor and video streams, designing catalogs and storage, building query and replay systems, and creating versioned datasets with lineage. The role requires operating scalable cloud infrastructure, batch processing pipelines, databases, and internal tooling for robotics and machine learning workflows.

Leads the design and lifecycle management of Oncology R&D data products, including data modeling, architecture, ETL/ELT pipelines, data integration, quality controls, governance, and AI-ready datasets. Partners with scientific, clinical, data science, and IT stakeholders to translate requirements into scalable solutions across clinical, preclinical, real-world, genomic, and omics data. Supports advanced analytics, ML, and GenAI use cases while ensuring interoperability, reliability, compliance, and operational performance.

Design and maintain automated testing across data pipelines, ETL/ELT workflows, REST APIs, backend services, and cloud data warehouses. Validate data integrity, schemas, transformations, reconciliations, performance, and reliability for ERP data flows. Integrate tests into CI/CD, manage synthetic test data, investigate discrepancies, support releases, and mentor junior quality engineers while promoting data-quality-focused testing practices.