Top Data Engineer Jobs in Portugal

Build and own backend services and APIs that deliver AI-powered, data-driven features. Model and expose sports data for applications and LLMs, optimize Postgres workloads, integrate LLMs with Snowflake/Postgres/internal APIs, and establish evaluation and observability practices for production AI features.

Design, build, and scale streaming and batch data pipelines and embedding workflows; integrate vector stores and AI platform services; implement observability, data quality, and governance; mentor engineers and lead production-ready AI data foundations supporting ad technology.

Design and build enterprise data warehouse, scalable data pipelines, integrations (OneStream, Workday, Oracle, Databricks), and AI/agent solutions (RAG, vector stores). Partner with Finance/HR/Legal/IT to translate workflows into automated, secure, maintainable enterprise data and AI services, and support analytics and reporting.

Own end-to-end data migrations from messy legacy municipal systems into GovWell: map and ingest data, validate and reconcile with SQL/Python, lead technical onboarding calls with government staff, ensure production data quality, and iterate on tools and playbooks with Product and Engineering.

Design, build, and maintain Databricks and cloud-based data ingestion, storage, and distribution solutions. Implement ETL/warehousing, pipeline orchestration, CI/CD, automated testing, and mentor junior engineers while collaborating across architecture and scrum teams to deliver scalable data products.

Lead site-based CSA activities for data centre projects: manage contractors, ensure compliance with design, safety and quality, coordinate stakeholders, produce reports, and support project leadership.

Design, build, and lead a scalable, configuration-driven data integration platform. Improve performance, automate monitoring and deployments, provide escalated support, and collaborate across product and engineering to deliver reliable, secure data integrations.

Design and implement enterprise visualizations and augmented-analytics capabilities (AI/ML, NLQ, NLG). Lead BI modernization, governance, self-service enablement, Analytics User Communities, and the Analytics Service Catalog. Mentor teams, drive adoption, ensure platform reliability, and align visualization standards with cloud and data architecture.

Lead architecture, implementation, and optimization of scalable data platforms, ETL/ELT pipelines, and data governance. Ensure security and compliance, mentor junior engineers, drive CI/CD and IaC for data workflows, and collaborate with stakeholders to deliver actionable analytics and reporting.

Build core data and AI infrastructure to ingest, model, and serve unstructured healthcare policy data. Design backend systems, data pipelines, LLM-powered RAG/embedding pipelines, evaluation frameworks, and platform APIs. Improve data quality, auditability, and system reliability while iterating rapidly with founders.

Build and maintain reliable Apache Airflow pipelines and workflow engines to move, schedule, and monitor data. Develop automation using Bash, Python, and Java. Work with distributed processing (Spark), containerization (Docker/containerd/Podman), and Git source control to support analytics and operational needs.

Define and lead the technical architecture for a lakehouse on AWS, build production pipelines and data models for time-series and multi-source data, introduce data governance and catalog foundations, author platform playbooks (ADRs, runbooks, Terraform modules), deliver projects end-to-end, and mentor engineers while representing the platform to senior leadership and non-technical stakeholders.

The Business Intelligence/Data Cube Developer will generate reports and data cubes, write custom ETL jobs, and support analytics for a federal agency.

Seeking a Sr. Big Data Engineer with 8 years of Java/Scala experience, Unix/Linux systems knowledge, and expertise in big data technologies and cloud deployment.

The Senior Software Engineer will design, build, and maintain backend services and REST APIs while supporting data pipelines and governance practices within the Infrastructure team.

The role involves building and maintaining full-stack systems for data feedback collection, translating research into production infrastructure, and enhancing model training and interaction.

The Staff Data Engineer will build and optimize data pipelines, ensure data reliability, collaborate with teams, and shape the data engineering landscape at CookUnity.

Design and build the data and analytics layer: schemas, pipelines, APIs, dashboards, forecasting and ML models. Deliver usage-based metering, ensure data correctness and observability, and ship customer-facing analytics while raising engineering standards.

The Senior Data Developer will extract, transform, and load data into MDM and data warehousing systems, optimize database designs and query executions, and enforce data strategy practices.

Design, build, and maintain ETL/ELT data pipelines and data models from SQL Server, PostgreSQL, SaaS, and internal apps. Ensure data quality, monitor ingestion, optimize Snowflake performance, collaborate with stakeholders to deliver dashboards and analytics, and recommend data architecture improvements.

Design, build, and deploy ML systems that link extracted document features to repayment and fraud outcomes. Run backtests, build evaluation and QA frameworks, develop adaptive/online learning for fraud and credit risk, and collaborate with customers to integrate models into underwriting workflows.

Design, build, and operate high-throughput real-time data integration and storage systems. Architect open table format layers at petabyte scale, build and optimize Spark-based batch and streaming pipelines, manage stream processing (Kafka/Flink), orchestrate pipelines (Airflow), and ensure performance, reliability, and cost efficiency across the data stack. Collaborate with platform and product teams and own end-to-end data infrastructure.

Design, build, and operate petabyte-scale data infrastructure: real-time ingestion, storage (open table formats), and serving. Lead performance, reliability, and cost optimization across Spark and streaming pipelines (Kafka/Flink), define data contracts, and establish best practices and tooling. Collaborate with platform, product, and deployment teams to deliver production-grade data systems on AWS GovCloud, Kubernetes, and Airflow.

Design, build, and operate petabyte-scale data infrastructure: real-time ingestion, storage (open table formats), and serving. Architect scalable Iceberg-based data lakes, optimize Spark pipelines and streaming (Kafka/Flink), drive reliability, cost efficiency, and data contracts; collaborate with product and platform teams and establish best practices and tooling.

Design, build, and maintain near real-time manufacturing dashboards and decision-support tools. Translate plant needs into intuitive visualizations, create reusable components, ensure data reliability and performance, collaborate with OT/IT and operations, and provide documentation, training, and governance-aligned deployment.