Top Data Engineer Jobs in Lisbon

Junior Data Engineer role supporting the development and maintenance of data pipelines and data products across cloud and on-premises platforms. Responsibilities include writing Python and SQL, using Snowflake, Azure, GCP, dbt, and orchestration tools; supporting testing, monitoring, governance, documentation, and optimization; and collaborating with engineers, data scientists, product owners, and business stakeholders.

Maintains Cloudflare’s global data center network infrastructure across remote colocation sites. Responsibilities include configuring network equipment, ensuring operational availability, managing contractors and vendors, creating SOPs and operational plans, supporting deployments and migrations, improving processes through automation and AI tools, troubleshooting infrastructure, and participating in a 24/7 on-call rotation. The role also involves basic program management, documentation, status reporting, and limited travel.

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, 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.

Designs and governs complex end-to-end data architectures across warehouses, lakes, lakehouses, and analytical platforms. Defines integration patterns, data models, governance standards, security, quality, lineage, metadata, and master data practices. Evaluates technology alternatives, documents architectural decisions, and guides multidisciplinary engineering teams. The role also provides architecture governance, technical leadership, documentation, and stakeholder communication, with emphasis on cloud platforms, scalability, resilience, performance, recoverability, observability, cost optimization, and Energy & Utilities solutions.

Develop and operate secure data pipelines for security events, integrate SOC and ITSM tools, and support AI-assisted incident response. Build SOAR automation, implement MLOps/AIOps practices, monitor model performance and drift, and address AI governance, security, and compliance risks. Collaborate with vendors and internal teams on production AI solutions while contributing to risk, ROI, and operational improvement decisions.

Data Engineer supporting a Business Intelligence team in Lisbon. Responsibilities include developing SAP BusinessObjects universes, designing data pipelines, managing BI security roles, automating artifact releases, supporting platform upgrades and migrations, monitoring database performance, and contributing to data infrastructure initiatives.

Develop and optimize Dataiku data workflows, automate integration and transformation processes, build Power BI dashboards, and create SQL and Python data solutions. Ensure data quality, consistency, and accessibility while collaborating with cross-functional teams to translate business needs into scalable technical solutions. The role requires autonomous delivery and maintenance of analytics, business intelligence, and data preparation projects.

Design and build robust, performant data pipelines capable of handling large data volumes. The role requires developing with Python and SQL, using Git for version control, applying data warehouse concepts, and understanding CI/CD principles. This is a hybrid position based in Lisbon, with 50% onsite and 50% remote work.

Designs, develops, and maintains Azure and Databricks data pipelines supporting European analytics. Responsibilities include data integration, modeling, ETL/ELT, quality monitoring, troubleshooting, legacy migration, deployment, performance optimization, and production support. Collaborates with analysts, product managers, and business stakeholders to translate requirements into reliable data products, while contributing to code reviews, documentation, engineering standards, and platform evolution.

Build and operate scalable data ingestion, activation, analytics, and machine learning pipelines. Develop Snowflake-based workflows, source connectors, transformations, incremental synchronization, data-sharing systems, and reliability tooling. Improve observability, data quality, schema evolution, and recovery processes while collaborating with engineering, product, infrastructure, data science, and customer-facing teams. The role includes moderate-scope project ownership, operational support, and potential on-call participation in a remote environment.

Designs, implements, manages, and optimizes batch and real-time data pipelines using structured and unstructured data. The role includes monitoring pipeline performance, supporting technology decisions, and enabling clients to access data for intelligent products across multiple industries. Requires expertise in Python, ETL, Spark, AWS, streaming technologies, SQL, data warehousing, architecture, and CI/CD, plus at least five years of relevant experience.

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.

Designs and operates scalable production data infrastructure for high-volume athlete, sensor, and time-series data. Builds streaming ingestion, graph models, low-latency ML feature systems, evaluation and monitoring infrastructure, and multi-tenant architectures. Ensures data provenance, lineage, observability, reliability, and correctness while collaborating with ML engineers, data scientists, sport scientists, and domain experts. Requires expertise in Python, SQL, streaming technologies, AWS, graph data modeling, causal inference, AI evaluation, and telemetry systems.

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.

Build and maintain Databricks-based data engineering solutions across EMEA and APAC. Responsibilities include developing agentic workflows, designing data models and contracts, implementing ingestion pipelines, supporting MDM and data quality initiatives, optimizing Spark and SQL workloads, and troubleshooting production failures. The role also involves AI governance, CI/CD automation, root cause analysis, and reporting on data quality improvements.

Designs, develops, and maintains scalable Python and GCP-based data pipelines, data lakes, and warehouses. Responsibilities include data modeling, ETL/ELT, testing, quality validation, monitoring, governance, security, documentation, and Agile collaboration. The role partners with engineers, analysts, data scientists, and stakeholders to deliver reliable data solutions, with French and English communication skills required.

Build and maintain high-performance data lakehouse infrastructure using Java, Rust, Apache Iceberg, and related technologies. Develop reliable maintenance workflows for compaction, snapshot expiry, manifest rewriting, and orphan file cleanup while ensuring data integrity under concurrency and failures. Maintain a Rust-based SQL query router, contribute connectors and open-source reviews, support production deployments, troubleshoot customer environments, and participate in on-call operations.

Build and maintain scalable data infrastructure, including data models, ETL/ELT pipelines, cloud architectures, orchestration, quality checks, and governance processes. Collaborate with engineers, analysts, data scientists, AI/ML teams, and business stakeholders to deliver reliable data solutions. Responsibilities also include troubleshooting, performance optimization, documentation, knowledge sharing, and evaluating improvements to data engineering practices and technologies.

Designs, builds, and operates scalable batch, streaming, and real-time data platforms and pipelines. Responsibilities include data lake and warehouse development, cloud modernization, data quality, governance, security, observability, CI/CD, infrastructure automation, troubleshooting, architecture, and technical leadership. Partners with product, engineering, analytics, data science, and business teams to support reporting, marketing measurement, AI/ML, experimentation, and customer insights.

Lead the design and evolution of Hiscox Europe’s Azure and Databricks data platform. Own complex data pipelines, architecture, modelling, governance, CI/CD, observability, reliability, and cloud cost optimisation. Provide technical leadership for a small engineering pod, mentor engineers, resolve platform incidents, support migrations, and collaborate with analysts, data management, IT, architecture, and business stakeholders to deliver scalable end-to-end data solutions.

Leads architecture and delivery of scalable data ingestion, activation, analytics, and machine learning data systems. Designs Snowflake pipelines, source connectors, transformations, incremental processing, and data-sharing workflows. Establishes standards for reliability, schema evolution, observability, testing, and recovery. Partners across engineering, product, infrastructure, and data science, provides technical leadership and mentorship, influences roadmaps, and supports high-availability production workflows.

Build and maintain scalable production data pipelines, machine learning data pipelines, and data transformation frameworks for financial crime detection solutions. Collaborate with product, R&D, data, analytics, customers, and partners; optimize large-scale data architectures; support feature engineering and root cause analysis; and train customer teams. The role requires customer-facing collaboration, English and Spanish fluency, and domestic and international travel when needed.

Design and deploy enterprise agentic AI systems, including MCP integrations, agent orchestration, secure tool execution, RBAC, identity propagation, policy enforcement, evaluation, and safety guardrails. Build production-ready platform adapters and integrations using Python, AWS, Docker, CI/CD, REST, SQL, and data modeling. Collaborate with developers, leadership, clients, and delivery teams while guiding adoption and maintaining engineering best practices.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.