Top Data Engineer Jobs in Portugal

Design and build a reliable, scalable enterprise data platform and end-to-end data pipelines at petabyte scale. Collaborate on architecture and code, prototype solutions, and own software engineering and product development to improve Starlink operations and analytics.

As a Senior Data Engineer at TRI, you will design and build robust data infrastructure and pipelines to support autonomy research and simulation workflows.

Lead the BI function owning Azure-based BI infrastructure and data pipelines, write and optimize complex SQL, maintain Power BI reporting and revenue analytics, lead and mentor BI analysts, collaborate with Sales/Finance/Operations, and ensure data quality and documentation standards.

Lead design and delivery of Databricks-based Lakehouse solutions and end-to-end data engineering projects. Build and optimize ETL, streaming and batch pipelines, integrate cloud and on-prem systems, mentor team members, support pre-sales, and ensure governance, security, and platform performance.

Responsible for designing, developing, and maintaining scalable data pipelines and infrastructure, implementing data governance, and collaborating with teams to meet data needs.

Design, develop, and maintain databases and scalable data pipelines. Build stored procedures, triggers, and ETL processes; perform testing, tuning, security, and data quality work. Collaborate with developers and stakeholders, document architecture, review code, and support reporting and integrations to enable restaurant and customer-facing systems.

Collect and analyze gesture interaction data in real-time to improve content usage experience. Build data pipelines and operate data storage for insights.

Lead architecture for a next-generation data layer for Agentic AI: design schemas and knowledge-graph ontologies, architect hybrid Snowflake/Kinetica/NoSQL environments, define ETL/ELT and CDC/streaming patterns, ensure cluster availability and performance, and act as the technical customer-facing lead aligning roadmaps and observability for real-time AI agents.

Own cloud software and data platform services end to end, including architecture, APIs, data models, pipelines, production operations, monitoring, incident response, and reliability. Build scalable systems for sensor-generated data, use AI-assisted development tools, mentor engineers, and collaborate with product managers to turn ambiguous requirements into production software. The role requires strong backend or full-stack engineering, cloud infrastructure experience, and ownership of systems from prototype through production.

Develop and maintain Palantir-based ETL and data transformation pipelines using Python and TypeScript. Ingest and format structured and unstructured data, integrate with external APIs, collaborate with data scientists on data models, perform O&M on ETL, produce documentation, conduct tier-2 triage, and design UI elements and custom functions.

Design, build, and maintain large-scale batch and real-time data pipelines on GCP (BigQuery, Dataflow, Dataproc, Pub/Sub). Migrate legacy ETL to cloud-native architectures, optimize query and pipeline performance, enforce data governance and security, and implement automated CI/CD for deployments while collaborating with cross-functional teams.

Design, build, and maintain scalable end-to-end data pipelines using Databricks, Spark, and cloud services. Develop transformation workflows, integrate diverse data sources, enforce data quality, monitoring, and observability. Leverage AWS (S3, Glue, Lambda, Redshift) and cloud warehousing; collaborate with data science, analytics, and business teams. Contribute to governance, security, and automation initiatives.

Design and build scalable data architectures and ETL pipelines to support cloud migrations. Develop batch and real-time data processing, ensure data quality and security, monitor and optimize pipelines, troubleshoot migration issues, document processes, and collaborate with cross-functional teams within Agile frameworks.

Design, build, and maintain scalable data pipelines and infrastructure. Integrate large datasets and ML models into production, leverage cloud and big-data frameworks, monitor pipeline health, troubleshoot issues, and collaborate with data scientists and stakeholders while documenting solutions.

Build, maintain, and operate ETL/ELT data pipelines for an enterprise data platform. Support deployment via IaC, monitor and troubleshoot pipelines, run data quality checks, document processes, and collaborate with senior engineers to test and deploy features in a mission-critical government environment.

Design, build, and maintain scalable GCP data architectures and ETL/ELT pipelines (batch and streaming) using BigQuery, Dataflow, Apache Beam, Cloud Storage, and Pub/Sub. Implement performance tuning, data quality and governance, secure access controls under federal requirements, and automated CI/CD for deployments. Collaborate with data scientists and infrastructure teams to modernize legacy systems and optimize data models for mission use.

Design, build, and deploy production-grade data and AI solutions on Databricks. Create scalable data pipelines, APIs, and Databricks Apps, integrate LLMs/AI APIs, deploy to cloud, implement CI/CD and governance (Unity Catalog), debug full-stack issues, and provide hands-on technical guidance to clients.

Build production-grade analytics systems that combine enterprise data, SQL, Python, dashboards, and agentic AI. Partner with leaders to analyze performance, define metrics, improve data quality, establish governance, and deliver executive insights and recommendations. Develop self-service data exploration, natural-language analytics, AI integrations, and reusable tools while collaborating with data engineering teams to resolve data issues.

Develop and maintain business intelligence reports, dashboards, and visualizations using BI platforms. Write SQL queries in Oracle and Databricks, support dimensional data models and data warehousing, gather reporting requirements, optimize query and report performance, and help manage documentation, version control, scheduled refreshes, and reporting enhancements. The role also supports AWS-based reporting solutions and collaboration with technical and business stakeholders.

Serve as a technical pre-sales advisor for AEC clients: lead discovery, design high-level integration and automation solutions, create proposals and architectures, communicate with executives, and hand off feasible solutions to delivery teams while identifying expansion opportunities.

Design, implement, and operate large-scale distributed data processing pipelines and ETL systems for healthcare data. Contribute across the software lifecycle, collaborate with cross-functional teams, debug production distributed-system issues, and apply cloud-native and DevOps practices to ensure reliable, performant data processing at scale.

Product-oriented senior ML role to build, deploy, and operate models and ML systems (classification, extraction, entity resolution, clustering, ranking, anomaly detection, forecasting, LLM pipelines). Define datasets, labeling, evaluations, monitoring, and collaborate with engineering and product to ship production-quality training/inference pipelines and model-serving infrastructure.

Lead design and scaling of ELT pipelines and analytics schemas on Snowflake. Build Looker views, CI/CD automation, and orchestration workflows (Prefect/Airflow). Ensure production reliability with monitoring (Datadog, Monte Carlo), partner with cross-functional teams, and provide technical leadership including code reviews, testing, and end-to-end ownership of data integrity and platform trustworthiness.

Design, implement, and administer operational data flows: ingest, clean, enrich, transform, secure, and move mission-critical data. Perform exploratory data analysis, troubleshooting, and collaborate with customers to ensure compliant, reliable data pipelines.

Build and operate scalable data ingestion, transformation, and connector frameworks; design and maintain a knowledge-graph-based data fabric; normalize and vectorize enterprise data for LLM/AI workflows; implement governance, lineage, access controls, and secure APIs to serve ML/agent pipelines.