Top Data Engineer Jobs in Portugal

Provides technical leadership for Finance, Actuarial, and Corporate Data platforms. Defines engineering strategy and standards, architects automated data pipelines, leads cloud migrations and ETL modernization, and drives AI-assisted development, DevOps, CI/CD, testing, and data quality practices. Manages senior data engineers and distributed teams, resolves complex engineering issues, governs technical delivery, and communicates strategies to executive stakeholders. Requires extensive enterprise data engineering, cloud platform, data warehousing, automation, and people leadership experience.

Designs and maintains scalable cloud data architectures, pipelines, ETL processes, data warehouses, databases, and data models. Ensures data quality, accuracy, security, availability, and consistency across sources. Develops SQL queries for analytics and reporting, and documents pipelines, schemas, architectures, and models. The role requires expertise with Python, Java, Scala, R, SQL, relational and NoSQL databases, cloud platforms, and Snowflake.

Build and maintain scalable ETL/ELT pipelines and data workflows using Python, PySpark, and SQL. Improve data quality, reliability, architecture, and governance while supporting analytics, reporting, and machine learning initiatives. Collaborate with data scientists, analysts, and business stakeholders, use Azure DevOps for CI/CD and release management, and mentor junior engineers. The role also involves API integration, cloud data platforms, testing, version control, and contributing to engineering best practices.

Designs and delivers large-scale, governed data pipelines and infrastructure across the full data lifecycle. Responsibilities include coding in Python and SQL, optimizing Airflow, dbt, AWS, Kafka, and Iceberg/Parquet workflows, implementing data quality and observability, contributing to architecture and tool evaluations, mentoring engineers, collaborating with stakeholders, and participating in on-call support.

Build and maintain Snowflake data models using dbt, improve data quality and observability, define data contracts, and support a modular healthcare data platform. Collaborate with analysts, data scientists, and software engineers to deliver trusted datasets. Responsibilities include pipeline monitoring, incident troubleshooting, performance and cost optimization, design reviews, documentation, and knowledge sharing. The role requires strong SQL and Python, data modeling, orchestration, CI/CD, cloud data warehouse, security, governance, and healthcare data knowledge.

Build and maintain Snowflake data models using dbt, improve data quality and observability, define data contracts, monitor pipelines, troubleshoot incidents, optimize data platform performance and costs, and document scalable data engineering practices. Collaborate with analysts, data scientists, software engineers, and other stakeholders while supporting healthcare data configurations and privacy requirements.

Develops and optimizes scalable ETL/ELT data pipelines and analytics solutions for DoD mission requirements. Processes diverse large-scale datasets, maintains pipeline operations, monitors system health, optimizes databases and costs, prototypes data capabilities, and collaborates with technical teams. Implements DoD STIGs and secure data retrieval methods across enterprise and potentially classified environments.

Lead the architecture and implementation of Shield AI’s knowledge-graph-centered data platform. Build production APIs, DataOps infrastructure, storage and compute integrations, ingestion paths, SDKs, and reference architectures. Establish data-modeling, schema evolution, lineage, observability, security, reliability, and lifecycle standards. Partner with autonomy, ML, testing, infrastructure, product, and customer teams while evaluating technologies and remaining hands-on with implementation, benchmarking, and debugging across cloud and customer-managed environments.

Design, build, and maintain large-scale ETL pipelines and data warehouses (Redshift) with strong data modeling, governance, and quality controls. Optimize systems for performance and reliability, integrate and deploy AI/ML models, troubleshoot and support Redshift, collaborate with data scientists and stakeholders, and produce technical documentation and knowledge-base articles.

Analyze manufacturing and spacecraft telemetry to identify anomalies, test failures, and degradation patterns. Build predictive models, SQL queries, operational dashboards, statistical process controls, and visualizations using Python and related tools. Document reproducible analyses in Jupyter notebooks and communicate findings to engineering, manufacturing, and mission operations teams. This is a three-month temporary, potentially convertible role supporting hardware production and spacecraft operations.

Lead Data SRE responsible for ensuring reliability, scalability, and performance of data platforms. Design monitoring, automate operations, optimize performance, respond to incidents, plan capacity, enforce security/compliance, document systems, and collaborate with engineering, data science, and product teams.

Designs and manages scalable data pipelines, contracts, transformation layers, and analytics models. The role focuses on data governance, observability, lineage, configuration validation, and collaboration with product engineering teams. It requires processing large datasets, integrating cloud and on-premises industrial technologies, and implementing streaming and message-queuing solutions, ideally in cybersecurity or industrial environments.

Leads the design, development, testing, and optimization of large-scale data engineering solutions. Responsibilities include building Python and PySpark ETL pipelines, orchestrating workflows, developing AWS cloud-native services, managing data lakes and Snowflake environments, and implementing testing, CI/CD, and secure engineering practices. The role also promotes responsible AI-assisted development, mentors engineers, participates in code reviews, and drives technical delivery initiatives.

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.

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.

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.