Top Data Engineer Jobs in Lisbon

Build and enhance SIXT’s AWS-based data platform and Data Shop. Design scalable data models, automate ELT pipelines, orchestrate workflows, and deliver data products supporting analytics, dashboards, and machine learning. Collaborate with data engineers, analysts, and scientists; contribute to platform strategy, data integrity, self-service capabilities, and AI-enabled automation. Use Redshift, dbt, Airflow, Python, SQL, and broader AWS services while applying agile engineering, testing, code review, and CI/CD practices.

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.

Design, build, and maintain scalable Python and GCP data pipelines, data lakes, and warehouses. Develop data models, transformations, orchestration, testing, monitoring, and data quality controls. Ensure governance, security, privacy, and compliance while documenting technical processes. Collaborate with engineers, data scientists, analysts, and business stakeholders in an Agile environment. French communication skills at B1 level and English are required.

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.

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 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.

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, 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 data pipelines and infrastructure for analytics, experimentation, and company metrics. Collaborate with machine learning engineers on training, quality assessment, and model release automation. Improve data delivery processes, automate manual workflows, and develop scalable data models. The role requires strong Apache Spark and Python expertise, SQL proficiency, Git experience, and familiarity with cloud data platforms and Kubernetes.

Build and maintain data pipelines, infrastructure, data models, and automation for analytics and machine learning workflows. Collaborate with ML engineers on model training, quality assessment, and releases. Improve internal data delivery processes and enable efficient access to large-scale organizational data.

Designs, develops, tests, and maintains integrations using Azure Integration Services. Builds Logic Apps workflows, APIs, asynchronous messaging solutions, data transformations, and integrations across enterprise platforms. Applies security and governance standards, troubleshoots issues, supports testing, documents data flows, monitors performance, and collaborates with architecture, product, and application teams in Agile delivery environments.

Design, develop, test, and maintain enterprise integrations using Azure Integration Services. Build Logic Apps workflows, APIs, Service Bus messaging solutions, data transformations, and integrations across CMS, DAM, PIM, ERP, CRM, and eCommerce platforms. Apply security and governance standards, troubleshoot issues, support testing, document integrations, monitor performance, and collaborate with architecture and delivery teams in an Agile environment.

Build and maintain scalable domain-focused data pipelines and datasets in a Lakehouse using Medallion architecture. Enable decentralized data-as-a-product, collaborate with product and engineering teams, promote data mesh and privacy-by-design, improve data quality, mentor engineers, and advance agentic systems and AI agents for data engineering.

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.

Lead mechanical engineering design and technical delivery for complex data centre projects. Develop resilient cooling, ventilation, automation, controls, and ancillary systems; coordinate with electrical and multidisciplinary teams; supervise engineers; produce and check detailed designs; manage resources and project deadlines; and support client relationships. The role also involves applying building physics, CFD, sustainable design principles, CAD, BIM, and digital design tools to mission-critical facilities.

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.

Build and optimize scalable data pipelines using Python, SQL, dbt, DuckDB, and PostgreSQL. Migrate interim workflows into production-ready architectures, define data contracts, maintain versioned data processes, and ensure analytical data quality for rule compliance. Develop reusable onboarding solutions for new countries and business segments, contribute to data architecture and reliability, and collaborate with cross-functional teams to deliver maintainable technical solutions.

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.

Build unstructured-data ingestion and context-layer pipelines for an investment group. Responsibilities include historical backfills, document parsing, entity resolution, deduplication, incremental API synchronization, embeddings, vector and graph storage, access scoping, lineage, PII protection, orchestration, monitoring, cost control, and operational runbooks.

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.

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.

Own the company’s end-to-end data platform, including ingestion, S3 data lakes, ClickHouse, dbt transformations, orchestration, and BI/CRM data delivery. Improve reliability, historical accuracy, cost, and metric governance while defining and defending business KPIs. Partner with Customer Success, Sales, Product, and leadership to translate business questions into durable metrics and establish data standards. This is a senior individual contributor role focused on hands-on engineering, analytical rigor, and stakeholder communication.

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.