Top Data Engineer Jobs in Lisbon

Build and scale customer-facing Analytics and Alerts APIs and near real-time alerting platform. Optimize analytical queries, collaborate with frontend/product teams, ensure reliability and run on-call for operational health.

Design, build, and operate a scalable logging and audit-log delivery platform. Develop high-performance connectors and integrations, ensure secure, compliant delivery to customer destinations, optimize pipeline throughput and latency, maintain operational health and monitoring, participate in on-call rotation, and collaborate on architecture and product requirements across teams.

Build and maintain high-throughput, low-latency distributed data systems across ingestion, storage, and retrieval. Optimize delivery pipelines, scale analytical database clusters (ClickHouse), develop GraphQL and log delivery solutions, monitor performance with observability tooling, troubleshoot production issues, and collaborate cross-functionally and with open-source communities to evolve the data platform.

Lead design and delivery of shared observability data platform capabilities to reduce developer friction, improve operational visibility, enable cost intelligence, and build reusable services. Drive architecture and technical leadership across teams, mentor engineers, contribute code, develop operational and self-service tooling, and participate in on-call rotation to improve platform reliability and developer experience.

Build and maintain reliable data models and ETL/ELT pipelines across AWS-based data lake layers. Ingest high-volume billing, CRM, network, and customer data; process Kafka events; develop dbt transformations; orchestrate workflows with Airflow; and implement data quality, monitoring, documentation, and reconciliation practices. Collaborate with stakeholders and engineers to deliver accurate, timely, traceable datasets for reporting and self-service analytics.

Designs, builds, and maintains data pipelines, workflows, databases, and ETL/ELT processes using Python and SQL. Develops Python-based tools, ensures data governance, security, quality, and reliability, and collaborates with cross-functional teams on data requirements. The role also involves monitoring pipelines, staying current with data engineering practices, and may include cloud platforms, big data tools, APIs, microservices, containerization, machine learning workflows, and data modeling.

Build and enhance SIXT’s AWS-based data platform and Data Shop. Design scalable data models, automate ELT pipelines, orchestrate workflows, and deliver data for dashboards, analytics, data products, and machine learning. Collaborate with data engineers, BI analysts, and data scientists; contribute to platform strategy, integrations, self-service capabilities, data integrity, and protection. Apply Python, SQL, testing, code reviews, GitHub, Agile practices, and Jenkins CI/CD while exploring AI-assisted development and agentic automation.

Build and improve data products across ETL pipelines, data warehouses, visualizations, testing, and cloud infrastructure. Design analytics services, improve predictive data quality, support production systems through DataOps and on-call participation, and mentor data engineers. Collaborate with product and engineering teams on technical approaches, metrics, roadmaps, and user stories. Work directly with customers to analyze raw data, create ingestion and enrichment pipelines, resolve data issues, and deliver visualizations.

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

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.

Own the financial audit data workstream for Printful and Printify. Deliver accounting reconciliations, investigate transaction discrepancies, build Snowflake transformations, extend Looker models, collaborate with Accounting and auditors, and document pipelines, corrections, and edge cases against fixed deadlines.

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.

Lead design and evolution of the data platform: architecture, schema design, governance, and infrastructure on AWS using Terraform. Ensure AI-ready, high-quality data; set standards, mentor engineers, and drive cross-team technical direction.

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

Design, build, and operate enterprise reporting and analytics platforms. Create scalable ELT/ETL pipelines, master data foundations, and lakehouse/warehouse semantic models. Deliver Power BI datasets and dashboards, implement data quality, governance, DevOps practices, and cost-management data solutions. Partner with finance and stakeholders and provide technical leadership across architecture, deployment, and run-state support.

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.

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

Design, build, and maintain scalable ELT/ETL data pipelines and data platforms. Develop Python transformations and orchestration, implement data models, ensure data quality and governance, integrate testing (UAT/QA), document systems, collaborate with data teams and stakeholders, and participate in code reviews and Agile ceremonies.

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.

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

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.

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.