Top Data Engineer Jobs in Lisbon

Build and operate production data pipelines supporting analytics, AI, and agentic workflows. Responsibilities include implementing canonical data models, maintaining Databricks or Snowflake platforms, monitoring reliability, responding to incidents, validating healthcare data mappings, improving performance and cost, and documenting architecture. Requires strong SQL and Python skills, cloud data platform experience, ETL/ELT orchestration expertise, and healthcare or pharmaceutical data experience.

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.

Designs and maintains scalable data pipelines, models, schemas, and database environments. Supports migrations, performance optimization, incident resolution, deployments, code reviews, and data quality. Collaborates with technical and business stakeholders, advises senior management on data architecture, and contributes to process automation, RPA, and continuous improvement initiatives.

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.

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.

Design and build robust, performant data pipelines capable of handling large data volumes. Apply Data Vault 2.0 and Kimball data warehouse concepts, develop using Python and SQL, manage code with Git, and understand CI/CD principles. The role is hybrid, with 50% onsite work in Parque das Nações, Lisbon.

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.

Designs, develops, tests, optimizes, and maintains scalable batch and near-real-time data pipelines and architectures. Ensures data quality, builds API integrations, improves internal data processes, and supports machine learning, data science, BI, and product initiatives. The role requires experience with data warehouses, relational and NoSQL databases, data modeling, event-driven architectures, SQL, Python, Airflow, Spark, and AWS data services.

Owns the design, development, operation, and governance of data pipelines and integration patterns supporting Medical Affairs AI products. Builds APIs and ETL/ELT solutions connecting enterprise systems to analytics platforms, RAG pipelines, vector databases, and GraphRAG applications. Ensures data quality, privacy, lineage, compliance, and protection of sensitive information. Partners with architecture, engineering, product, and business stakeholders to deliver reusable, production-grade data integrations.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

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.

Own the data engineering side of DoiT’s integrations framework by building and maintaining third-party billing and usage API integrations. Ensure data correctness, completeness, normalization, reconciliation, and support for negotiated rates. Manage orchestration, scheduling, backfills, and pipeline operations while using AI throughout development. Collaborate with product, support, and engineering teams to expand vendor coverage and improve cloud and SaaS spend visibility.

Develop and maintain enterprise data warehouse and ETL/ELT solutions across GCP and on-premise SQL Server platforms. Translate business requirements into scalable, end-to-end data solutions, applying data modeling and warehouse best practices. Support code and configuration management, CI/CD pipelines, Agile delivery, and automated deployments. Collaborate with stakeholders, contribute to architecture decisions, and potentially mentor junior team members.

Own data engineering for a cloud and SaaS billing integrations platform. Build and maintain third-party billing API integrations, operate ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation and quality checks. Use AI throughout the engineering workflow and collaborate with product, support, and platform teams to expand vendor coverage and improve customer cost visibility.

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.

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.

Designs, builds, and maintains scalable Databricks Lakehouse data pipelines and batch or streaming workflows. Optimizes data processing performance, scalability, and cost while supporting analytics, machine learning, and business intelligence use cases. Collaborates with engineers, architects, data scientists, and stakeholders to deliver production-ready platforms. Contributes to data quality, governance, security, compliance, and architecture, while mentoring junior engineers and supporting client-focused data platform strategies.

Develop internal data services, including a feature store, scalable ELT/ETL pipelines, and data processing and storage integrations. Design high-load event-driven systems and streaming pipelines using Python, SQL, cloud platforms, databases, orchestration tools, APIs, and messaging technologies. Perform operational tasks and contribute to robust system design within a fully remote fintech engineering team.

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.

Build and maintain data architecture, data lakes, data products, analytics, reporting, and machine learning solutions. Partner with stakeholders to identify use cases, engineer and analyze structured and unstructured data, implement monitoring and lifecycle management, and communicate insights through visualizations. Ensure data quality, governance, compliance, and reliable delivery using cloud technologies, especially AWS, alongside SQL, Python, and business intelligence tools.

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.

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

Lead the design, implementation, and operation of enterprise data platforms, scalable ELT/ETL pipelines, master data structures, lakehouse and semantic models, and Power BI reporting. Own a cost-management data solution, establish data quality and governance controls, and implement DevOps practices. Partner with finance, operations, and business stakeholders to deliver secure, trusted, high-performing data products and provide technical leadership across architecture, deployment, and support.