Top Data Engineer Jobs in Lisbon

As a Mid Data Engineer, you will design data tools, automate processes, and collaborate with clients to enhance their data needs.

The Data Engineer will design data pipelines and architecture, optimize machine learning models, and collaborate on data projects for international brands.

Design, implement, and operate batch and real-time data pipelines. Ingest and process structured, semi-structured, and unstructured data using Spark, streaming platforms, and ETL tools. Monitor and optimize pipeline performance, support stakeholders on technology decisions, and enable clients to build data products across industries.

Lead design and architecture of a petabyte-scale, real-time data platform powering AML/KYC and fraud products. Own ingestion, streaming, lakehouse, feature/serving stores, observability, data quality, and lineage. Partner with Engineering, Data Science, Product and SRE, coach engineers, set company-wide data standards, and enable ML/agentic AI workloads.

Refactor and maintain Python codebases, enforce modular/SOLID design, write unit/integration/performance tests, migrate to Python 3, optimize distributed data processing (Spark/Hadoop/Cloudera), implement CI/CD (GitLab CI/CD, Jenkins), manage containerized deployments (Docker, Virtuozzo), and integrate tooling like SonarQube, Nexus, Jira API, and PyPI/pip within an Agile team.

Build and maintain data infrastructure and DevOps for data platforms. Design, deploy, monitor, and optimize Airflow pipelines, data lakes/warehouses, and distributed Hadoop/Spark environments. Provide data access to analysts, perform code reviews and optimizations, deploy production DAGs, document regulatory analyses, and support business requirements and architecture improvements.

Design, build, and optimize scalable data systems and ETL pipelines to prepare data for BI and AI/ML projects. Collaborate with BI teams, aggregate data from multiple sources, and ensure availability and performance of data infrastructure.

Design, build, and maintain scalable data pipelines and storage solutions. Optimize data collection, processing, and quality for large datasets. Collaborate with data scientists, analysts, and stakeholders to meet data needs and ensure data integrity, security, and architecture across cloud, hybrid, or on-prem environments.

Lead industrialisation and production lifecycle of AI/ML/LLM use cases: deploy and containerise models, build CI/CD pipelines, monitor model performance, ensure traceability, implement validation frameworks, and enforce security and regulatory compliance in collaboration with data science, security, and IT teams.

Design, develop, and maintain Elasticsearch solutions; build Python scripts and automation for data processing; implement Grafana and Prometheus monitoring dashboards; support observability architecture design; deploy and manage Kubernetes clusters; collaborate with technical and business stakeholders and propose continuous optimizations.

Design, build, and optimize robust, high-volume data pipelines and data warehouse solutions. Apply Data Vault 2.0 and Kimball methodologies, develop in Python and SQL, use Git, and follow CI/CD best practices to deliver performant data workflows and insights.

The Python Data Engineer will design and maintain scalable data pipelines for data-driven decision making, collaborating with cross-functional teams and ensuring data quality and compliance.

As a Data Engineer, you'll build and optimize data infrastructure, manage pipeline migrations, and implement data governance and monitoring tools that improve data quality and observability.

Design, build, and operate core data platform infrastructure (Kubernetes, AWS, Terraform). Enable self-service data ingestion, ETL/ELT pipelines, cloud data warehousing, orchestration, and support analytics and ML use cases while ensuring data integrity and platform scalability.

Design, build, and maintain AWS-based lakehouse solutions and medallion architecture using Iceberg and Glue. Implement CDC and streaming ingestion with Debezium and Kafka. Develop PySpark batch/distributed jobs on EMR/Glue, orchestrate workflows with Airflow, ensure data quality/governance, and collaborate on infrastructure and IaC practices to deliver reliable business-ready datasets.

Design, build and maintain data integrations and pipelines on Databricks and Azure Data Factory. Cleanse and curate data, monitor batch jobs, engage stakeholders to translate requirements, document solutions, and support the cloud and legacy on-prem platforms while enabling self-serve reporting.

Design and implement data pipelines and solutions using Azure services, focusing on data modeling, transformation, and compliance in a collaborative environment.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.

The Senior Product Data Engineer will build, iterate, and launch data products, focusing on large-scale data processing and ETL/ELT pipelines, using various technologies and implementing AI features.

Design, build, and operate scalable Databricks data pipelines and Lakehouse data models using SQL, Python, and PySpark. Implement ELT/ETL and streaming patterns, data quality checks, Unity Catalog governance, performance optimization, monitoring, documentation, and operational support.

As a Data Engineer, you'll analyze user data and product performance, create dashboards, and utilize SQL and Python to derive insights for business strategies.

Design, build, and maintain scalable data pipelines and storage for fraud detection. Support fraud analytics, feature engineering, model integration and serving, pipeline monitoring, and documentation. Collaborate with data, product, and engineering teams to enable detection, reporting, and operationalisation of fraud models.

The Principal Data Engineer will improve data pipeline architecture, optimize data flows, and support data projects for various teams in the company.

Design and maintain AWS-based data pipelines, processing vehicle data, implementing ETL/ELT infrastructure, and collaborating with stakeholders to deliver analytics-ready datasets.

The Data Engineer designs big data architecture, optimizes machine learning models, collaborates on data projects, and builds data pipelines.