Top Data Engineer Jobs in Portugal

Architects, builds, and scales data pipelines, ETL processes, data models, and platforms using dbt, Snowflake, Airflow, and Fivetran. Leads data engineering best practices including CI/CD, data quality, governance, and observability. Collaborates with Data Science, Engineering, Product, and business stakeholders; mentors engineers and data scientists; contributes to architecture and technical decisions; and participates in on-call support.

Designs, builds, deploys, and supports scalable Databricks data pipelines and lakehouse solutions for observability, infrastructure, and security data. Develops PySpark and Structured Streaming workloads, establishes data quality and governance standards, optimizes performance, and improves reliability and operational excellence. Partners with security, infrastructure, observability, and platform engineering teams to onboard data sources, implement access controls, advance CI/CD practices, and deliver trusted enterprise data products.

Designs, builds, and maintains scalable Azure data pipelines and ELT frameworks using Databricks, SQL, Python, and PySpark. Implements Azure DevOps CI/CD, monitoring, security, infrastructure automation, and production support. Collaborates with BI teams on Power BI datasets, documents data solutions, participates in Agile delivery, and mentors junior engineers. The role requires strong data modeling, structured and semi-structured data expertise, performance tuning, and data governance awareness.

Build and optimize enterprise-scale Snowflake data solutions, including ingestion, ETL pipelines, transformation, quality assurance, production deployment, legacy warehouse migration, and workflow automation. Establish data governance, compliance policies, and data quality rules while supporting cloud-based architectures and efficient data consumption.

Designs scalable Databricks solutions and ETL/ELT pipelines while administering MariaDB and SQL Server environments. Responsibilities include database design, performance tuning, backup and disaster recovery, security and access management, data modeling, quality and lineage, and troubleshooting complex data issues. The role collaborates with stakeholders and technical teams, translates requirements into secure solutions, provides technical direction, and delivers demonstrations and training for Department of Transportation support.

Designs and operates an AWS-native lakehouse supporting analytics, reporting, visualization, and AI/ML. Builds batch and streaming ingestion pipelines using Python and PySpark, Apache Iceberg data structures, governance and metadata services, quality controls, security, observability, and CI/CD automation. Optimizes performance, scalability, reliability, and cost while collaborating with cross-functional technical teams and documenting architecture, operations, and secure configurations.

Develops, tests, documents, operates, and troubleshoots application and medical-device interfaces using Epic Bridges, HL7, X12, DICOM, FHIR, and interface engines. Translates business requirements into technical specifications, supports FHIR adoption, manages small-to-medium integration projects, maintains compliance and architecture artifacts, develops testing strategies, mentors junior staff, and provides technical support. The role also includes documentation, technology trend briefings, on-call support, and protection of patient health information.

Supports secure data pipelines, ETL/ELT workflows, data transformations, and analytics-ready datasets in a classified DoD environment. Collaborates with data scientists, engineers, and mission stakeholders while using Python, SQL, Spark, Databricks, and comparable platforms. Performs data validation, modeling, quality practices, testing, documentation, and Agile development. The role is fully on-site in a SCIF at Camp Smith, Hawaii, and requires an active Top Secret/SCI clearance before starting.

Designs, builds, deploys, and maintains scalable data platforms, warehouses, lakes, ETL/ELT pipelines, and batch or streaming workflows. Optimizes cloud data lake and pipeline performance, manages data governance and lifecycle solutions, and collaborates with multidisciplinary agile teams. The role supports mission-driven client projects involving disparate structured, unstructured, and event-streaming data.

Design, build, and deploy agentic AI solutions (Google Gemini, Claude, ADK); maintain BigQuery data warehouse and ETL pipelines; troubleshoot tracking across Google Analytics, Omeda, Piano, and Salesforce; create dashboards and monitoring to ensure data quality and measure agentic impact across Sales, Marketing, Editorial, and Finance.

Lead design, build, and operate scalable Databricks/Delta Lake data pipelines and Kimball dimensional models using dbt. Manage governance with Unity Catalog, optimize performance and cost, operationalize ML with MLflow, and coordinate/quality-check offshore vendor engineering. Mentor engineers, uphold standards, and deliver trusted data products that power analytics, reporting, and MLOps workflows.

Lead the technical design and operation of consumer-facing data products on a Databricks lakehouse. Own medallion architecture, dimensional modeling, ODCS data contracts, data quality, observability, CI/CD, incident response, and production standards. Write and review Python and SQL, guide code reviews, mentor engineers, collaborate with product, platform, governance, and ML teams, and ensure compliant, reliable delivery in a regulated insurance environment.

Designs and maintains scalable batch and streaming data pipelines, data warehouses, cloud data platforms, validation frameworks, and governance processes. Supports analytics through data modeling and Tableau enablement, resolves complex data issues, delivers architecture prototypes, collaborates with business and audit stakeholders, owns projects end to end, and mentors engineers. Requires advanced SQL and Python, AWS, Snowflake or Databricks, orchestration, transformation tooling, regulatory compliance, and extensive data engineering experience.

Build secure, scalable data platforms and pipelines supporting product and customer needs. Develop robust, tested Java and Python code; design distributed data systems, modern SQL/NoSQL structures, data lakes, and warehouses; collaborate with business and technology stakeholders; document solutions; and use AI-powered development tools throughout the software development lifecycle.

Own the architecture and development of data-intensive web applications powering customer insights. Provide hands-on technical leadership, mentor engineers, conduct architecture and code reviews, resolve complex distributed-system issues, and deliver performant, maintainable data products. The role involves ETL and/or RAG development, full-stack work across React, TypeScript, Next.js, PostgreSQL, and C#/.NET, with opportunities to operate cloud infrastructure and observability systems.

Designs, builds, and maintains scalable ETL/ELT pipelines, cloud data infrastructure, storage, and batch or real-time processing workloads. Optimizes queries, PySpark jobs, pipelines, performance, and cloud costs. Partners with BI, analytics, data science, and AI teams to develop dimensional data marts supporting reporting and machine learning. Implements governance, data quality validation, lineage tracking, security controls, and automated infrastructure and code deployments using CI/CD practices.

Build and optimize scalable data pipeline architecture, assemble complex datasets, improve data delivery infrastructure, and support data initiatives across engineering, product, BI, and data science teams. The role requires production-scale software engineering experience focused on data and ML processes, expertise with Spark, Kafka, SQL, and a programming language such as Python, Scala, or Java, plus knowledge of databases, data modeling, and ML operations.

Designs, develops, tests, deploys, and maintains big data software systems processing network, DNS, and web content data. The role requires Java or Python development, distributed systems, data storage, performance tuning, testing automation, CI/CD, cloud-native architectures, incident response, and technical leadership in an Agile environment.

Leads the architecture, development, governance, and adoption of Growth[x]'s enterprise data and AI foundation. Responsibilities include building knowledge cortexes, integrating governed data sources, improving data quality, designing AI-enabled workflows, productizing prompts and playbooks, developing training, managing a technical team, and coordinating with Growth leadership, IT, security, and platform stakeholders. The role is hands-on and requires expertise in agentic AI, data engineering, cloud platforms, data warehousing, BI, and enterprise solution architecture.

Provides senior technical leadership for enterprise data engineering platforms, with emphasis on Snowflake, ETL/ELT pipelines, data quality, governance, observability, reliability, and production operations. The role guides architecture and engineering standards, supports incident response and on-call operations, improves platform scalability and resilience, and partners with engineering, infrastructure, security, governance, architecture, and business teams. It is a hands-on leadership position without direct people-management responsibility.

Provides electrical engineering support for data center design, construction, expansions, retrofits, and upgrades. Reviews designs, proposals, calculations, equipment submittals, construction deliverables, and standards. Performs power, load, arc flash, coordination, PUE, lighting, and underground heat analyses. Supports commissioning, factory testing, customer meetings, troubleshooting, value engineering, MOP reviews, and development of electrical and MEP standards.

Designs, develops, and maintains Databricks-based ETL pipelines, data integration solutions, data warehouses, and reporting datasets. Builds batch and streaming architectures using Python, Spark, SQL, and Delta Lake; optimizes performance and costs; migrates legacy SSIS and SQL Server processes; maintains SSRS reports; implements data quality controls; and collaborates with business and technical teams supporting analytics and AI/ML workloads.

Designs, builds, and optimizes scalable end-to-end data pipelines and curated data products using Snowflake, SQL, and Python. Translates business needs into reliable technical solutions, ensuring data quality, governance, performance, and cost efficiency. Troubleshoots complex issues, contributes to architectural decisions, uses AI-assisted engineering tools, and collaborates with engineering, analytics, product, and business stakeholders. The role also includes code reviews, documentation, mentoring, and influencing data engineering best practices.

Develop and manage data pipelines, integrations, analytics solutions, ontologies, business object models, and enterprise data architectures using Palantir Foundry. The role supports data governance, quality, lineage, operational workflows, technical solution design, and cross-functional collaboration with technical and business stakeholders. Federal or Department of Defense experience and Agile experience are preferred. A DOD Secret clearance is required.

Designs, builds, and operates enterprise data lakes, warehouses, lakehouses, ETL/ELT pipelines, databases, and reporting platforms. Prepares governed, model-ready data for AI and machine learning, supports feature stores and embedding workflows, administers relational databases, optimizes queries and dashboards, and ensures data quality, security, availability, and disaster recovery. Collaborates with technical and business teams, reports to senior management, and mentors data engineers.