Top Data Engineer Jobs in Lisbon

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.

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.

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, build, and maintain data ingestion and transformation pipelines into Snowflake. Troubleshoot incidents, run backfills, validate data, write tested dbt models, migrate data from MSSQL/Oracle, monitor pipelines, and document solutions for team use.

Develop and operate end-to-end data features on an AWS-based big data platform: design data flows, build proofs-of-concept, deploy and monitor solutions, troubleshoot, document in Confluence, and support data extraction and exploratory analysis.

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

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.

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.

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.

Design, build and operate data ingestion, transformation and delivery solutions within a Microsoft Fabric/Azure data lakehouse. Develop PySpark notebooks, pipelines and SQL/T-SQL processes, implement Bronze/Silver/Gold architectures, CI/CD for Fabric artefacts, and contribute to data quality, metadata, lineage and governance to support reporting, analytics and AI use cases.

The Principal Data Engineer will improve data pipeline architecture, optimize data flows, and support data projects for various teams in the company.

Design, build, and maintain scalable batch and streaming data pipelines, internal tools, and APIs to support analytics, product features, and ML. Collaborate with product and engineering to improve data infrastructure, ensure reliability, testing, and observability in a cloud-native environment.

Design, build, and maintain scalable ELT data pipelines with Airflow; develop dbt data models and tests; optimize Athena tables/queries and data workflows; provision infrastructure with Terraform; ensure data quality and reliability; collaborate with Analytics, BI, and Engineering; troubleshoot pipeline and data quality issues.

Build a greenfield data platform and unified Lakehouse integrating high-volume payment, marketing, and operational data across AWS and GCP. Design ingestion pipelines, create dbt medallion-layer transformations in BigQuery, and own Airflow orchestration, lineage, backfills, retries, alerting, infrastructure as code, and cost controls. The role requires autonomous ownership in a fully remote, asynchronous environment, with data mesh and Starburst Galaxy experience as preferred qualifications.

Build and ship full-stack features for a data intelligence SaaS platform, spanning backend services and frontend interfaces. Use AI coding assistants to accelerate development while applying sound engineering judgment. Participate in architecture discussions, code reviews, testing, incident response, troubleshooting, documentation, and knowledge sharing. Partner with product managers to translate requirements into technical solutions. The role emphasizes SaaS architecture, AWS cloud fundamentals, Docker, Kubernetes, CI/CD, agile delivery, autonomy, and production ownership.

Design and optimize Snowflake data models, customer 360 views, identity resolution, segmentation, and CDP-ready schemas. Build pipelines syncing customer data to Braze, Hightouch, and related platforms. Improve performance, cost efficiency, data quality, governance, consent management, and audience activation while collaborating with CRM, marketing, analytics, and technical stakeholders.

Lead data and analytics projects: design dimensional models, build Power BI dashboards, run exploratory analysis, capture client requirements, develop end-to-end data pipelines on Azure, test and assure solutions, support client decision-making, and contribute to business generation and people development.

Lead delivery of end-to-end data and analytics solutions: build dimensional models, develop Power BI dashboards, perform exploratory analysis, design Azure-based data pipelines, capture client requirements, run scenario modelling, test and assure solutions, prepare client communications and lead project teams.

The Data Engineer will build reusable Data Pipelines, manage data ingestion, work with cross-functional teams, and design solutions aligned to Data Architecture in Financial Services.

Build and maintain Camunda 8 backend data-layer services across relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, streaming, and secondary-storage solutions; investigate production data issues; improve performance and operability; and deliver engineering initiatives collaboratively with Product, QA, Support, and SRE. Participate in L3 support and on-call rotations.

Landskill is seeking a Big Data Engineer with experience in big data tools and programming. Duties include data modeling, ETL, and integration, with a focus on personal and professional development.

Lead mechanical due diligence and feasibility for data center sites and deals. Validate cooling topology and equipment strategy (air, liquid, hybrid), assess capacity, vendor lead times, and integration with budgets and schedules. Define mechanical readiness gates, deliver validated scope to Delivery, coordinate with stakeholders and vendors, and help build repeatable evaluation standards for AI/hyperscale infrastructure acquisitions.

Build and maintain scalable data pipelines, transformations, and analytics-ready models using dbt, SQL, Python, and Snowflake. Implement ingestion, CDC, API integrations, testing, monitoring, and observability. Collaborate with analysts and stakeholders to define metrics, ensure data governance and compliance, and optimize data platform reliability.