Top Data Engineer Jobs in Lisbon

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.

Staff Software Engineer responsible for designing, building, and operating distributed market data systems. The role focuses on real-time blockchain data ingestion, streaming, normalization, storage, APIs, reliability, data correctness, observability, incident recovery, and production support. The engineer will lead technical decisions, mentor teammates, translate ambiguous requirements into designs, and use AI-assisted development throughout the software lifecycle.

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 end-to-end data applications, APIs, internal tools, and user interfaces using AI-assisted development. Work across Python backends, React frontends, data models, Databricks, dbt, pipelines, cloud infrastructure, testing, security, and performance. Review AI-generated code, establish guardrails, improve engineering workflows, and help shape application architecture and technical direction.

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 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 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.

Build and maintain scalable GCP data pipelines, BigQuery data models, enterprise semantic layers, and CI/CD workflows for a modernized marketing data platform. Rebuild legacy pipelines, support DataHub adoption, enforce data quality, optimize performance and costs, and enable secure multi-brand marketing reporting. Collaborate with product, Martech, analytics, and data engineering teams.

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.

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

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

Build and own the architecture for Dune’s curated data lifecycle, including dependency-aware orchestration, backfills, schema evolution, retries, recovery, data quality, and alerting. The role combines backend software engineering with large-scale data systems work across Go, Kotlin, Rust, Python, and SQL. Responsibilities include designing ingestion and curation contracts, making architecture decisions, solving ambiguous problems, and leading work that other engineers can execute.

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, 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.

Design and build durable, distributed data-streaming infrastructure that decouples process data from Camunda’s live execution engine. Develop customer-facing integrations such as Kafka connectivity, support analytics data consumption at scale, and make architecture decisions around consistency, durability, and storage. Participate in production support and on-call rotations while collaborating with Product, Data Layer, Distributed Systems, QA, SRE, and Support teams. Lead ambiguous, cross-functional engineering initiatives from design through delivery.

Build and maintain Camunda 8 backend data-layer services spanning relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, and streaming systems; improve performance, indexing, and production tooling. Lead engineering initiatives from solution design through delivery, collaborate across teams, provide L3 support, and participate in on-call rotations. Investigate production data issues and contribute to schema evolution, workflow automation, and CI improvements.

Design, implement, and test cloud-based server-side software in Python. Optimize code performance, maintain unit and integration tests, and work with AWS, Docker, DevOps, and CI/CD tools. Preferred experience includes reverse engineering public APIs, PostgreSQL, Django ORM and DRF, network protocols, and web or mobile technologies.

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.

Designs, implements, modernizes, and optimizes enterprise-scale cloud data platforms for financial-services clients. Responsibilities include building Microsoft Fabric solutions, implementing Medallion Architecture, developing end-to-end ETL/ELT pipelines, and enabling analytics, reporting, and AI workloads. The role also establishes data governance, security, quality, lineage, monitoring, and scalable data models; collaborates with client stakeholders in agile environments; documents technical designs; and mentors or leads technical workstreams.

Design and implement data pipelines and solutions using Azure services, focusing on data modeling, transformation, and compliance in a collaborative environment.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.