Top Data Engineer Jobs in Portugal

Build and operate scalable data pipelines and trustworthy data models supporting Layla’s AI-powered travel products. Own data quality, reliability, observability, experimentation, and model-evaluation foundations. Enable production AI/ML systems through feature and data availability, evaluation datasets, and monitoring. Partner with Product, Analytics, and ML teams to deliver dependable insights, establish engineering best practices, and mentor other engineers.

Build and operate reliable, scalable data pipelines and trusted data models for Layla’s AI-powered travel-planning and booking products. Own data quality, observability, experimentation, model-evaluation datasets, feature availability, and production monitoring. Partner with Product, Analytics, and ML teams to deliver dependable insights and support responsible AI/ML systems. Mentor engineers and promote testing, documentation, and reusable data-engineering practices.

Develop data products, pipelines, and business intelligence reporting solutions using cloud technologies, large-scale databases, streaming systems, ETL/ELT, and data warehouses. Translate business requirements into technical solutions, analyze high-volume datasets, troubleshoot SQL and pipeline issues, support user acceptance testing, and identify insights, patterns, anomalies, and trends. The six-month co-op includes mentorship, leadership development, and project presentation opportunities.

Design, build, and operate large-scale ETL pipelines, data lakes, and data platforms processing billions of daily events. Own data quality, freshness, reliability, monitoring, and observability for foundational datasets. Develop self-service ETL frameworks and tooling, contribute to architecture and technical strategy, and partner with product, engineering, machine learning, data science, and leadership teams to deliver scalable data solutions.

Leads enterprise software engineering initiatives supporting financial forecasting, planning, and reporting. Designs, codes, tests, documents, and supports Oracle Essbase 21c solutions and associated infrastructure. Develops ETL solutions using Python, PySpark, and object storage; manages platform capacity, availability, performance, security, middleware, and production issue resolution. Establishes engineering standards, mentors teams, resolves complex technical issues, and supports modernization toward cloud-based data engineering and analytics platforms.

Designs, develops, and maintains data platforms, pipelines, and integration solutions across on-premises and cloud environments. Responsibilities include resolving data quality and security issues, building ELT processes, developing data scripts and relational or non-relational models, and monitoring data lake performance and scalability. The role also supports analytical and operational decision-making through reliable data solutions and collaborates with cross-functional teams.

Own and evolve business-critical data products, designing scalable data services, APIs, models, and batch or streaming pipelines. Lead projects through implementation, deployment, monitoring, and production support while applying AI tools throughout the software development lifecycle. Collaborate across engineering, product, data, and business teams, contribute to architecture and governance, improve reliability, and mentor earlier-career engineers.

Build and operate scalable growth data pipelines, models, feature datasets, and measurement systems supporting paid acquisition, ad bidding, experimentation, attribution, and audience targeting. Partner with Growth, Marketing, Analytics Engineering, and Data Science to deliver reliable data products, productionize machine-learning workflows, maintain data quality and observability, and improve platform performance, cost efficiency, and developer experience.

Build and operate ETL and data-ingestion pipelines consolidating OT cybersecurity data into Databricks. Develop Python and SQL solutions, data-quality controls, monitoring, and automation; analyze vulnerabilities and cyber risks; and create Power BI dashboards supporting remediation prioritization. Collaborate with cybersecurity, manufacturing, plant, and data engineering teams, document data processes, and improve OT vulnerability-management visibility and reporting.

Architect, deploy, and operate core data platform services (Airflow, Superset, Terraform). Build CI/CD pipelines, automate with Python, manage cloud infrastructure, implement security and compliance, develop dbt data models, catalog assets in OpenMetadata, and mentor junior engineers.

Build and operate production-grade ELT pipelines from Benchling, Salesforce, and third-party systems into Snowflake using dbt. Own ingestion, transformation, warehouse, BI, governance, monitoring, testing, access controls, privacy, cost, and performance. Partner with AI engineering to provide trusted data for internal AI applications and agentic workflows, contribute to warehouse and metrics-layer architecture, and translate cross-functional stakeholder needs into scalable data solutions.

Build and operate production-grade ELT pipelines from product, Salesforce, and third-party systems into Snowflake using dbt. Ensure testing, monitoring, schema versioning, governance, privacy, access controls, data quality, cost, and performance. Support AI initiatives with trusted data, contribute to warehouse and semantic-layer architecture, and translate cross-functional stakeholder needs into scalable data solutions.

Build and maintain data pipelines integrating laboratory instruments, automation systems, operational platforms, APIs, robotics, databases, and files. Develop SQL transformations, data models, validation checks, orchestration, monitoring, and alerting to support analytics, dashboards, troubleshooting, and AI systems. Document infrastructure and datasets for regulatory compliance while collaborating with engineering, laboratory operations, data science, and automation teams. This is an on-site role with on-call and occasional weekend or holiday support.

Design, build, optimize, and support scalable batch and real-time data pipelines and enterprise data products. Develop reusable frameworks, ingestion automation, testing, CI/CD, and data-quality solutions while ensuring governance, security, lineage, observability, and policy-compliant data segregation. Troubleshoot production issues, improve performance and cost efficiency, and collaborate with engineers, data stewards, scientists, product managers, and business stakeholders across global teams.

Design, build, test, and maintain secure, scalable data pipelines and architectures. Work with Python, Databricks, SQL/NoSQL, and enterprise AI capabilities to deliver trusted data collection, storage, access, and analytics. Validate AI-assisted outputs, update data models, customize tools, and ensure controls, traceability, and data sensitivity handling.

Leads enterprise data engineering architecture and SAP data conversion initiatives. Designs scalable ingestion, transformation, storage, and delivery patterns; builds BODS ETL workflows; manages extraction, cleansing, mapping, validation, reconciliation, and loading. Establishes automation, governance, quality, lineage, security, observability, and infrastructure-as-code practices. Coordinates analysts, SAP teams, engineers, business owners, vendors, and consultants through mock conversions, cutovers, rollback, and production operations while coaching teams and resolving critical issues.

Design, build, test, and maintain scalable, secure data pipelines and architectures. Update data models, advise on/configure tools, validate controls, use SQL/NoSQL, and apply enterprise AI capabilities to accelerate and validate data engineering workflows while ensuring traceability and data sensitivity handling.

Build and maintain analytics data models, warehouse tables, views, and automated pipelines using dbt and Snowflake. Ensure data quality, consolidate business-unit data, develop ThoughtSpot dashboards, and analyze complex datasets to support product and operational decisions. Partner with product, engineering, and operations stakeholders, define metrics, improve self-service analytics, leverage AI tools for automation, and document data processes.

Build and maintain data pipelines, ingestion workflows, transformations, and data products using Python, SQL, and cloud data platforms. Test and troubleshoot systems while applying Git, software testing, and CI/CD practices. Collaborate with senior engineers and cross-functional stakeholders, use AI-assisted tools, and deliver reliable data solutions in a fast-paced startup environment.

Provide technical leadership for analytics engineering across enterprise platforms. Design and optimize scalable Databricks data pipelines using Python, PySpark, and SQL; develop governed Power BI solutions, semantic models, dashboards, and executive reporting. Partner with stakeholders to define metrics, deliver AI-enabled insights, and improve decision-making. Ensure data quality, lineage, security, and governance while optimizing models, queries, and pipelines. Lead large-scale technical initiatives, influence architecture, and guide analytics engineers.

Design, develop, and operate a production agentic data platform using Spark, Kafka, Airflow, Snowflake and AWS. Write secure high-quality code across frontend and backend, leverage enterprise AI-assisted development and LLMs (Claude Code, agents, RAG, embeddings), build autonomous agents and MCP server components, apply secure/responsible AI practices, troubleshoot systems, and own full-stack application/platform delivery with CI/CD, containers, and observability.

Build and maintain dbt data models and pipelines, implement real-time ingestion with PySpark and AWS Glue, and process distributed data in Databricks. Apply software engineering practices, review Git pull requests, partner with data scientists and stakeholders, translate requirements into Jira stories, and support data governance, quality, integrations, and deployment across the enterprise.

Build, optimize, and support scalable batch and streaming data pipelines for enterprise workforce, billing, and interaction datasets. Develop cloud-native solutions with AWS, Databricks, PySpark, Kafka, Kubernetes, and Airflow while processing tens of terabytes of data. Provide production support, performance tuning, troubleshooting, and data platform automation. Collaborate across Product, Governance, Analytics, and Engineering teams, support data warehousing and reliability practices, participate in architecture decisions, and mentor junior engineers.

Develop and optimize cloud-based data lakes and pipelines supporting analytics and machine learning. Build automated workflows using Python, SQL, Dagster, dbt, and comparable tools; transform raw data for reporting; and maintain secure, compliant handling of PII and PHI in a regulated SDLC. Collaborate with data scientists, product teams, and engineers to solve technical challenges and improve the data platform.

Serve as functional DBA for Oracle and MongoDB across environments. Partner with app teams on schema design, data modeling, and query optimization. Diagnose and resolve database performance, integrity, and incident issues. Validate database changes for releases, maintain documentation/runbooks, and support data migration and transformation for enterprise-scale applications.