Top Data Engineer Jobs in Portugal

Build and maintain scalable, reliable data pipelines and ingestion processes; support data integration, quality, governance, and cloud-based analytics platforms; troubleshoot partner/customer data environments; document standards and reference architectures; automate processes and ensure security and compliance for enterprise data solutions.

Lead the design and implementation of a unified semantic layer and enterprise data models, transforming SAP, Workday, Salesforce, Anaplan and other sources into AI-ready datasets. Establish modeling standards, testing, observability, and automation; collaborate with engineers and business stakeholders to deliver scalable, high-performance data solutions for AI agents and analytics.

Support development of Command & Data Handling (CDH) electrical subsystems including requirements decomposition, interface management, design analysis, hardware integration, testing, and on-orbit operations. Coordinate with vendors, manage electrical interfaces and telemetry, perform verifications and risk mitigation, and contribute to costing and schedule development for programs.

Manage, monitor, and optimize enterprise ETL/ELT data integration pipelines and platforms. Administer integration environments, implement observability and alerting, triage incidents (including 24/7 rotation), automate operations, maintain runbooks, and collaborate with cross-functional teams to ensure reliable data flow into the lakehouse and downstream applications.

Develop and implement a data management strategy, oversee data governance and architecture, ensure compliance, and manage vendor relationships to support organizational goals.

The Senior Data Engineer will standardize and reuse data, develop features, create ETL processes, and collaborate with cross-functional teams.

Design and implement cloud-native data pipelines and metadata readers on OCI, build AI agents and integrate mission data with LLMs using RAG/MCP, develop production geospatial solutions, create Node.js dashboards connected to Oracle Databases, and ensure secure, scalable cloud architectures for defense customers.

Provide L1/L2 support for data pipelines and monitoring, triage and resolve incidents, perform routine operational tasks, collaborate with developers and business users, maintain runbooks, participate in on-call rotation, and drive root cause analysis and continuous improvement.

Own and evolve end-to-end data pipeline architecture across ingestion, transformation, modeling, and serving. Lead platform improvements (cost, compute efficiency, access control), CI/CD, monitoring, and incident response. Drive cross-team technical initiatives, mentor engineers, and integrate AI tooling to improve workflows while remaining hands-on building and debugging production pipelines.

Design, build, and scale cloud lakehouse data products (Databricks/Snowflake) from ingestion to analytics-ready datasets using medallion architecture. Develop reusable Python frameworks, Spark pipelines, and dbt/SQL transformations. Implement data modeling, governance, observability, validation, and SLA monitoring. Troubleshoot production issues, adopt AI to accelerate engineering, and provide technical leadership, code reviews, architecture decisions, and mentoring for global engineering teams.

Lead design, management, testing, and real-time support of LEO satellite data handling subsystems. Develop and maintain operations procedures, scripts, and tools; perform telemetry monitoring, anomaly detection, recovery, and commanding. Support onboard algorithm development and troubleshooting; ensure satellite safety and operational readiness.

Own end-to-end data pipelines (ingest, dbt transformations, BigQuery warehousing) and consolidate the analytics stack into a single source of truth. Build and maintain dbt models with tests and documentation, establish data quality and observability, own product analytics and instrumentation, partner with GTM and marketing on reporting, and prepare for high-volume/streaming ingestion.

Lead design and execution of data migration, archival, and disaster recovery for a cloud-based court case management modernization. Own migration strategy, data lifecycle and retention, DR/HA design and drills, tooling evaluation, documentation, and coordination with Data Engineering, Governance, Security, and stakeholders to ensure auditable, resilient, and compliant migrations.

As a Backend Engineer, you'll build backend services for candidate-job matching and marketplace workflows, focusing on APIs, data models, and operational systems.

Lead engineer for TJX's Enterprise Test Data Management platform responsible for designing, implementing, and supporting scalable TDM solutions (K2View), enabling data provisioning, masking, synthetic generation, CI/CD integration, and mentoring teams. Collaborates with architecture, security, and infra to ensure compliance, performance, and automation across SDLC and test automation workflows.

Design, build, test, and maintain high-quality data pipelines and storage systems. Collaborate with architects, analysts, scientists to enable data ingestion, transformation, real-time analytics, and data reliability. Write efficient code, unit/integration tests, and apply DevOps/SDLC practices while ensuring data security and cloud infrastructure best practices.

Lead design and delivery of scalable data platforms and pipelines on Google Cloud. Build and review Python/SQL code, drive data architecture using BigQuery and GCP tooling, own ingestion/transformation/orchestration, lead client technical discussions, prototype solutions, mentor engineers, and implement IaC, CI/CD, observability, and governance.

Design, build, and run data-heavy features for financial products, improve data models, pipelines, migrations, and multi-tenant systems. Work in a TypeScript monorepo, operate serverless AWS infrastructure, integrate LLM APIs, monitor production, and lead pragmatic technical decisions.

Lead design, build, and operate a secure, scalable data platform to support analytics, MLOps, and BI. Architect data warehousing (Databricks, dbt, Spark), build ETL pipelines, optimize cloud storage and processing in GCP/AWS, implement LLM infrastructure and serving, create observability and BI dashboards, and ensure data governance and regulatory compliance (HIPAA/SOC-2). Collaborate with Data Science, ML, App, and DevOps teams.

Own and build the end-to-end desktop and server platform for human-feedback data collection: integrate Electron and a C#/.NET recorder, implement anti-cheat/fraud detection, operate the payments ledger in Java/Spring Boot with Postgres, build Next.js admin tools in TypeScript/React, harden deployments on GCP and Cloudflare, run marketplace A/B cohort mechanics, and build compliance tooling.

Lead development, tuning, and optimization of incrementality and attribution models for podcast advertising. Build, test, and refine statistical models on large datasets, collaborate with product and data teams, translate findings into actionable insights, and improve measurement methodologies to quantify ad impact.

Design, build, and maintain production-scale data systems and web-scale datasets (Spark/Flink) to support personalization ML. Drive dataset/schema design, collaborate with algorithm teams, and improve distributed big-data processing for member experience.

Design and operate large-scale data platforms in the cloud, develop data pipelines, support analytics and machine learning, and mentor junior engineers.

Design, build, and operate production-grade realtime and batch data pipelines and internal data products. Ensure data correctness, tooling for backfills and reports, and partner with product/business teams to prioritize data acquisition and improvements. Work across query engines and streaming frameworks to support analytics, monitoring, and debugging for product and business stakeholders.

Design, build, and maintain scalable Python-based ETL/ELT data pipelines and Airflow workflows implementing Medallion (Bronze/Silver/Gold) Lakehouse patterns. Ensure data quality, performance tuning, monitoring, CI/CD, and cross-team collaboration to support analytics, reporting, and ML use cases while adhering to governance and security standards.