Top Data Engineer Jobs in Portugal

Architects and evolves enterprise-scale batch, real-time, and event-driven data platforms and pipelines. Leads data modeling, distributed processing, data quality, governance, observability, CI/CD, security, monitoring, incident response, and cost optimization. Provides technical direction across hybrid cloud environments, mentors engineers, leads critical initiatives and code reviews, manages technical debt, evaluates new technologies, and partners with business and technical stakeholders. Participates in on-call rotations and supports distributed and offshore engineering teams.

Principal Engineer responsible for architecting scalable, secure, near-real-time data pipelines and modernizing legacy systems onto GCP. The role develops data models, APIs, microservices, automation, and CI/CD workflows; supports AI and advanced analytics integrations; and advances data governance, lineage, compliance, access control, and quality monitoring. The engineer partners with product, analytics, and engineering teams to deliver data solutions for digital, retail, and operational use cases.

Designs and maintains scalable data architecture, warehouses, ETL/ELT pipelines, and data platforms. Responsibilities include ensuring data quality, integrity, governance, accessibility, reliability, and performance; supporting customer onboarding and analytics tools; contributing to architecture and engineering best practices; troubleshooting complex data issues; and participating in on-call rotations. The role collaborates with engineering, product, solutions, business intelligence, and other cross-functional teams in healthcare and social care data environments.

Develop machine learning, statistical, graph-based, and data-mining algorithms for national-security missions. Build, optimize, and validate AI/ML and large language models; create feature vectors, experiments, simulations, visualizations, and predictive analytics; and implement prototypes in production frameworks. Collaborate with subject-matter experts to automate analysis, guide analytic development, and deliver data-driven insights. Experience with LLMs, RAG, agents, GPU computing, and active TS/SCI clearance with polygraph is required.

Assists with monitoring, operating, inspecting, troubleshooting, and maintaining critical data center mechanical and electrical systems. Performs preventive maintenance, responds to alarms and emergencies, supports equipment installation and upgrades, manages work orders and vendors, maintains records, and follows safety and regulatory procedures. The role requires working on-site, climbing ladders and rooftops, lifting up to 50 pounds, and working rotating overnight, holiday, and emergency schedules.

Builds and maintains production-grade ETL/ELT pipelines across legacy on-premise systems and AWS/Azure cloud platforms, centralizing data in Snowflake for analytics, machine learning, feature stores, and generative AI. Develops vectorization and embedding pipelines, optimizes Snowflake performance and cloud costs, manages cross-platform orchestration, and implements data quality and observability frameworks. Partners with IT, security, MLOps, data science, and infrastructure teams to deliver scalable, governed data solutions.

Own the analytics data layer by translating stakeholder needs into certified dimensional models, governed metrics, semantic layers, dashboards, and reports. Build with dbt, SQL, Databricks, Power BI, and Airflow while ensuring data quality, observability, reconciliation, documentation, and dependable orchestration. Partner closely with finance, operations, and client-facing teams, automate recurring workflows, and use AI-assisted engineering practices in a fully remote environment.

Design and build resilient data pipelines integrating files, APIs, services, databases, and mission sources. Develop extraction, normalization, enrichment, validation, cataloging, routing, ingestion, and event-driven workflows. Preserve geospatial metadata, provenance, lineage, security attributes, and data quality. Apply AI, RAG, MCP, and agentic capabilities to improve data discovery and access. Build Node.js applications and Oracle Database integrations while collaborating with engineering, cloud, security, AI, and mission teams.

Design and operate the data quality layer for a Databricks lakehouse using DQX. Translate legacy SQL validation logic into automated checks, quarantine invalid records, establish quality gates, integrate checks with batch and streaming pipelines, store results in Delta and Unity Catalog, build dashboards and alerts, and deploy version-controlled rules through CI/CD.

Design, build, and maintain ETL/ELT pipelines and API integrations to ingest, cleanse, transform, and analyze infrastructure and cloud data. Produce analytics, dashboards, and executive reports; automate workflows and support near-real-time processing. Follow SDLC/DevOps practices (Git, CI/CD), ensure data governance, quality, lineage, and support incident, release, and compliance activities.

Designs and implements Databricks Unity Catalog access controls, security architecture, identity governance, data classification, and compliance automation. Leads RBAC and ABAC models, dynamic views, row- and column-level security, masking, service principal integration, and infrastructure-as-code deployments using Terraform or Databricks Asset Bundles. Establishes security guardrails, templates, tagging standards, and scalable workflows while partnering with data, platform, and workspace engineering teams.

Architect and manage cloud environments in classified regions using infrastructure-as-code and automation. Build and maintain ETL/ELT pipelines integrating security, vulnerability, event, and log data across ACAS, Elastic, and Splunk. Implement data quality, normalization, enrichment, governance, security, and compliance controls. Optimize ingestion and automate workflows, troubleshoot integrations, maintain schemas and documentation, and support dashboards and visualizations. Collaborate with cybersecurity teams to deliver scalable data solutions.

Build, deploy, and operationalize complex data pipelines and solutions using Databricks, cloud platforms, Python, and SQL. Responsibilities include data transformation, cleansing, governance, security, quality, source analysis, testing, defect resolution, and support for AI, machine learning, and business intelligence. The role also involves cross-team collaboration, stakeholder education, and delivery of resilient data products.

Design, build, test, and maintain cloud-based data pipelines and ETL processes using Python, Databricks and Spark. Develop SQL queries and dashboards, automate workflows, integrate code with GitHub/GitLab CI/CD, troubleshoot data quality, and collaborate with stakeholders to deliver scalable, secure data solutions.

Leads hands-on design, development, and optimization of AI data movement and distributed storage systems. Responsibilities include integrating NVIDIA NIXL and DDN Infinia with GPU inference platforms, optimizing GPU-to-storage I/O using GPUDirect Storage, RDMA, and NVMe-over-Fabrics, developing KV cache and multi-tier storage strategies, benchmarking production systems, resolving performance bottlenecks, influencing distributed inference architecture, and mentoring engineers.

Interns will work with Data Solution Engineers to solve business problems and build data assets for actuarial and product management teams. They will receive training in Snowflake, SQL, Azure Data Factory, Azure Databricks, SAS, SQL Server, Python, and Power BI; collaborate with mentors; complete project tasks; and participate in community service. The role is fully in-office in West Bend or Madison, Wisconsin.

Designs and maintains the College’s enterprise data and AI platform. Develops integrations, governed data pipelines, warehouses, APIs, MCP services, dashboards, analytical applications, and AI-supported workflows. Establishes testing, monitoring, security, access controls, data governance, and evaluation standards. Partners with institutional offices to translate business needs into secure technical solutions while investigating unfamiliar systems, APIs, schemas, and workflows.

Own the enterprise product data architecture for Allegion’s Global Software Solutions business. Define unified data models, schemas, pipelines, APIs, catalogs, governance standards, and device telemetry architecture across multiple business units. Lead data onboarding, platform convergence, architecture audits, compliance, and engineering enablement. Partner with enterprise data, AI, identity, and product engineering teams to make governed product data usable for analytics and agentic AI.

Designs, builds, and operates scalable batch and streaming data pipelines, ELT/ETL workflows, and cloud data infrastructure. Ensures data reliability, quality, observability, lineage, performance, and cost efficiency across enterprise domains. Partners with analytics engineers, BI teams, and business stakeholders to deliver analytics-ready datasets. Establishes engineering practices including version control, CI/CD, automated testing, infrastructure-as-code, architecture standards, and documentation.

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.