Top Data Engineer Jobs in Lisbon

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 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.

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.

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 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.

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.

Senior analytics engineer responsible for data governance, analytical modeling, and platform efficiency in BigQuery/GCP. Build reusable, trusted datasets, define semantic layers and analytics architecture, optimize performance and cost, establish data contracts and standards, and partner cross-functionally to support monetization, LiveOps, payments, and AI/ML workflows.

Design, build and operate scalable data models and ELT pipelines on AWS (Redshift, S3, Glue). Implement dbt transformations, orchestrate workflows with Airflow, apply engineering best practices (Python, SQL, CI/CD), enable self-service data products, and collaborate with BI and Data Science teams to deliver analytics and ML-ready data while ensuring data protection.

Develop and operate end-to-end data features on an AWS-based big data platform: design data flows, build proofs-of-concept, deploy and monitor solutions, troubleshoot, document in Confluence, and support data extraction and exploratory analysis.

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.

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.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

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

Design, build, and maintain scalable batch and streaming data pipelines, internal tools, and APIs to support analytics, product features, and ML. Collaborate with product and engineering to improve data infrastructure, ensure reliability, testing, and observability in a cloud-native environment.

Design, build, and maintain scalable ELT data pipelines with Airflow; develop dbt data models and tests; optimize Athena tables/queries and data workflows; provision infrastructure with Terraform; ensure data quality and reliability; collaborate with Analytics, BI, and Engineering; troubleshoot pipeline and data quality issues.

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.

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

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, implement and maintain scalable data pipelines, storage, ETL and data warehousing on Azure. Manage data integration, architecture, security, governance and cloud-based services (Azure Databricks). Use scripting (Python) and data technologies (Spark, SQL/NoSQL) to support analytics and Power BI integration.

As a Data Engineering Intern, you'll design and maintain data infrastructure, build ETL pipelines, optimize workflows, and support data quality efforts while learning modern data tools.

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

The Data Engineer will build reusable Data Pipelines, manage data ingestion, work with cross-functional teams, and design solutions aligned to Data Architecture in Financial Services.

Build and maintain scalable data pipelines, transformations, and analytics-ready models using dbt, SQL, Python, and Snowflake. Implement ingestion, CDC, API integrations, testing, monitoring, and observability. Collaborate with analysts and stakeholders to define metrics, ensure data governance and compliance, and optimize data platform reliability.

The Data Engineer role focuses on programming, data integration, and working within cloud environments while ensuring team collaboration and professional development.

The role involves designing and migrating data processes from SQL Server to GCP, developing ETL pipelines, optimizing databases, and collaborating with teams to ensure smooth transitions and data integrity.