Top Data Engineer Jobs in Portugal

Develop and migrate reporting assets from SAP BusinessObjects to QlikView and Qlik Sense. Analyze and rebuild Web Intelligence, Crystal Reports, and universes; debug legacy and new reports; optimize SQL, scripts, expressions, data models, and load processes; validate output accuracy; document migration logic; and collaborate with stakeholders on requirements and report specifications.

Supports migration of reporting assets from SAP BusinessObjects to Qlik by rebuilding, testing, validating, documenting, and troubleshooting reports. Responsibilities include creating dashboards, writing SQL queries, updating Qlik scripts and expressions, tracking migration changes, migrating scheduled jobs, and supporting user acceptance testing and training documentation under senior developer guidance.

Lead the architecture, development, governance, and reliability of AssetMark’s Azure and Snowflake data platform. Design scalable ETL/ELT pipelines, write and review Python and SQL, establish engineering standards, oversee DataOps, security, compliance, and FinOps, and integrate AI/ML and GenAI capabilities. Mentor engineers, lead technical design sessions, and partner with Data Science, Product, Security, Compliance, and business stakeholders to deliver governed, production-grade data products.

Design, build, and support enterprise data platforms, semantic/ontology models, and scalable data pipelines. Implement data quality, lineage, metadata, CI/CD, governance (including RLS), and observability. Provide technical leadership, mentor team members, and collaborate with stakeholders to enable analytics, AI, and LLM-driven solutions.

Designs and leads data architecture, models, metadata standards, integration strategies, storage solutions, and virtualized data pipelines. Partners with business stakeholders, technical teams, and product owners to define data requirements and digital ecosystem recommendations. Supports data governance, quality, security, visualization, and analytical systems while guiding technology assessments and digital transformation initiatives.

Leads architecture and engineering standards for Curaleaf’s data and analytics ecosystem. Designs scalable Snowflake platforms, ingestion and transformation pipelines, data models, governance, testing, CI/CD, security, observability, and cost optimization. Partners with engineering, analytics, data science, governance, and business teams to deliver trusted data products. Provides technical direction, mentorship, incident leadership, and cross-functional alignment as a hands-on principal individual contributor.

Designs and operates data infrastructure for a clinical trial platform. Builds pipelines that extract, normalize, validate, and link information from clinical documents and publications into Aurora and GraphDB. Develops embeddings, vector search, RAG workflows, data-serving APIs, lineage tracking, and regulatory audit trails. The role also requires AWS-based orchestration, clinical data quality controls, biomedical knowledge graph modeling, and deployment automation using infrastructure-as-code and containerization.

Design and build production AI, data analytics, and cloud solutions for product engineering. Own end-to-end architecture, agentic systems, evaluation standards, data pipelines, AWS platforms, APIs, and user interfaces. Develop full-stack applications, establish technical standards, mentor engineers, manage developers, and partner with engineering teams to move prototypes into adopted production systems. The role requires hands-on Python, SQL, modern front-end, LLM orchestration, RAG, data engineering, cloud, and cost-aware architecture expertise.

Designs and leads spacecraft data architectures for Blue Ring satellites, including command and telemetry databases, parameter data, onboard storage, uplink and downlink flows, and data prioritization. Collaborates with flight software, spacecraft engineering, operations, systems engineering, and test teams to implement, automate, verify, and scale data solutions across missions. Requires extensive spacecraft development and operations experience, programming expertise, and knowledge of CCSDS standards and command sequencing.

Leads technical direction for end-to-end machine learning and data engineering systems. Builds, evaluates, deploys, and monitors production ML models and feature pipelines; establishes data quality, governance, observability, and responsible AI standards. Partners cross-functionally with Product, Design, Data Science, Analytics, and platform teams, while mentoring engineers and driving technical execution without formal people management.

Designs and maintains scalable data pipelines, warehouse models, and analytics solutions. Builds natural-language data interfaces using GenAI and LLM techniques, optimizes SQL, improves data quality and observability, and supports distributed data platforms on AWS or GCP. Partners with engineering, analytics, business, and product teams, contributes to shared codebases, establishes development standards, and participates in production on-call and incident response.

Manage and enhance Splunk infrastructure, including data onboarding, ingestion pipelines, dashboards, queries, alerts, user management, retention, and performance troubleshooting. Integrate diverse data sources, normalize and route security data, support security operations, collaborate with stakeholders, and provide user training. The role requires Splunk architecture expertise, Linux and Windows administration, data pipeline development, and an active TS/SCI clearance with potential polygraph requirements.

Designs, executes, and supports enterprise data migrations into SAP S/4HANA. Responsibilities include data extraction, transformation, validation, reconciliation, cutover, replication, system refreshes, troubleshooting, and documentation. The role requires collaboration with functional teams, business owners, and infrastructure stakeholders while ensuring data accuracy, completeness, compliance, and governance across large-scale SAP implementations.

Design, implement, and maintain cloud-based data pipelines; integrate disparate data sources; optimize data lakes/warehouses and Spark workflows; automate Quote-to-Cash/Quote-to-Fulfillment processes; collaborate with platform, analytics, and business stakeholders to deliver scalable, reliable data solutions while ensuring data quality and security.

Build and oversee scalable data platforms, warehouses, lakehouses, pipelines, ETL processes, and dimensional models using Databricks, Snowflake, AWS, Spark, Python, PySpark, and SQL. Integrate tracking data, optimize queries, implement governance and monitoring, and support AI/ML models. Partner with cross-functional teams, deliver dashboards and insights, mentor engineers and analysts, and automate infrastructure deployment using Terraform and CI/CD practices.

Designs, develops, and optimizes scalable data pipelines, architectures, and cloud-based data solutions using Azure services and Databricks. The role involves data integration, quality, security, privacy, governance, testing, incident support, and continuous improvement for high-volume data environments. The engineer collaborates with analytics, product, architecture, governance, and business teams, contributes to infrastructure planning and engineering standards, and mentors less experienced data engineers.

Design, build, and maintain distributed data platform components (messaging, DB, data warehouse), automate provisioning/backups, troubleshoot performance/reliability, participate in on-call rotation, mentor junior engineers, and contribute to the technical roadmap and platform improvements.

Leads technical strategy and hands-on delivery across data platforms, engineering, analytics, AI, machine learning, governance, and automation. Manages roadmaps, backlogs, technical programs, and internal data and AI products while coordinating teams, vendors, and stakeholders. Develops prototypes, analyses, integrations, dashboards, and automations; evaluates technical risks and performance; builds business cases; tracks KPIs and value realization; and supports budgets, investments, planning, and strategic priorities for the Data and AI organization.

Build and operate production data pipelines and enterprise data services supporting AI models, agents, and mission applications. Integrate structured and unstructured data across relational, document, vector, and graph systems; establish data quality, lineage, metadata, governance, and secure access practices; and collaborate with AI, platform, government, and commercial engineering teams. Deploy services in Kubernetes and classified environments while developing reusable ingestion, orchestration, documentation, and operational patterns.

Build and operate production data integrations and ETL pipelines using AWS Glue, PySpark, Python, SQL, and ClickHouse. Responsibilities include designing schemas, integrating unfamiliar data sources, monitoring pipeline health, debugging incidents, improving observability, supporting restricted-environment deployments, and participating in on-call operations. The role collaborates with forward-deployed engineers and requires full-time onsite work in New York City, occasional travel to Arlington, and eligibility for a U.S. government security clearance.

Build and operate data integrations, ETL pipelines, schemas, monitoring, and production services in a restricted, air-gapped government environment. Develop extensions, deploy software across low- and high-side systems, monitor data quality and pipeline health, troubleshoot incidents, perform root-cause analysis, and create runbooks. Serve as the on-site engineering liaison between the customer and Arlington-based engineering and DevSecOps teams. The role requires independent judgment, customer-facing field engineering experience, and operational ownership.

Analyzes and resolves vehicle commissioning, software validation, and diagnostic issues in a production environment. Responsibilities include testing software and vehicle functions, analyzing CAN logs and ECU data, tracking defects, developing fault-finding processes, creating dashboards and reports, troubleshooting data loggers, and collaborating with validation, production, and after-sales teams. The role also supports scalable validation data systems and process optimization.

Build scalable data infrastructure, pipelines, streaming solutions, and governed data models supporting regulatory reporting, financial operations, analytics, and AI-powered applications. Partner with Engineering, Compliance, Analytics, and business teams to deliver accurate, auditable data products. Improve data quality, observability, platform reliability, and engineering productivity while mentoring teammates and contributing to modern cloud data platform strategy.

Own business metadata and data governance activities across Finance, Commercial, HR, and Marketing. Partner with data stewards and domain owners to define, certify, map, validate, and publish business terms, metrics, KPIs, and data assets in Unity Catalog. Facilitate definition workshops, resolve terminology conflicts, improve Excel and Python intake processes, establish governance workflows, and document metadata lifecycle procedures.

Build and evolve scalable AWS data platforms, enterprise data warehouses, pipelines, models, and integrations. Develop Redshift schemas, implement data quality and observability controls, optimize data jobs, and support lineage and documentation. Partner with data science, analytics, and business teams to deliver reliable datasets. Participate in Agile ceremonies, code reviews, automation, and mentoring while promoting modern, reusable data engineering practices.