Top Data Engineer Jobs in Portugal

A 12-week internship focused on building data pipelines, machine learning workflows, and distributed systems. The intern will use Python, ML frameworks, Spark, Docker, and Kubernetes to develop, train, deploy, and scale models and intelligent data solutions. Responsibilities include supporting reliable, performant AI products and gaining experience with cloud platforms, scalability, fault tolerance, and production ML deployment.

Drive technical sales for Microsoft’s cloud and AI data platform through demos, proofs of concept, hackathons, and architecture workshops. Advise enterprise customers on secure, scalable database and analytics architectures, migrations, and deployments. Build relationships with platform leaders, resolve technical blockers, collaborate with engineering, and promote Azure data technologies including Fabric, Databricks, Purview, and Azure databases.

Designs, analyzes, documents, integrates, and migrates complex environmental datasets for AirHub business lines. Responsibilities include analyzing legacy databases, creating logical and physical data models, documenting mappings and business rules, developing migration specifications and API integrations, supporting data governance and testing, and producing technical documentation. The role also requires close collaboration with business stakeholders in an Agile delivery environment.

Validates complex enterprise data processing, transformations, migrations, reconciliations, reporting, business rules, APIs, and event-driven workflows. Develops data validation strategies, automated database validation scripts, migration tests, and reconciliation reports. Partners with data architects and engineers while mentoring junior testers. The role requires advanced SQL, automation, ETL validation, relational data modeling, large-scale migration testing, and Python programming.

As a Data Engineer, you will design, implement, and maintain data systems for Notifications at Whatnot, ensuring data quality and performance while collaborating with cross-functional teams on data architecture and workflows.

Senior Data Engineer responsible for designing and deploying scalable data pipelines, ensuring critical data availability, movement, transformation, and storage. The role requires Python, Databricks, ETL, relational database, and Agile Scrum experience, supporting AI and machine learning applications for national security missions. The position is primarily onsite three to five days per week and requires an active Secret clearance and U.S. citizenship.

Lead financial data engineering initiatives involving commissions, incentive programs, and backend financial systems. Build scalable, accurate, and reliable data pipelines, platform tooling, partner integrations, analytics infrastructure, and data science infrastructure. Shape technical strategy and roadmaps, collaborate with Finance, Product, Data Science, and Engineering, mentor engineers, contribute code, lead incident response, and establish engineering standards.

Designs and leads cloud-native data engineering and architecture solutions for Supply Chain analytics. Responsibilities include building AWS data pipelines and lakehouse platforms, implementing Apache Iceberg and Medallion Architecture, developing Redshift data products, applying data mesh and governance principles, integrating SAP and Blue Yonder data, and leading technical engineering teams.

Build and operate scalable backend infrastructure for financial data, including ingestion, transformation, integration, and serving systems. Design high-performance distributed pipelines handling millions of documents and datapoints, support AI and retrieval systems, and integrate heterogeneous financial sources. Ensure secure, compliant, reliable handling of customer data while collaborating with globally distributed engineering teams and influencing technical direction.

Build and integrate deep learning models into software pipelines, collaborate with product and engineering teams, support data ETLs, and deploy solutions to cloud-hosted environments.

Architects and develops scalable big data pipelines, ETL processes, real-time analytics frameworks, and cloud data infrastructure for battery and time-series data. Ensures data quality, designs storage and data models, supports production AI/ML workflows, and integrates model outputs into pipelines. Translates business and engineering needs into functional specifications while coordinating internal and external stakeholders.

Develop and implement quality strategies, automated testing frameworks, and validation practices for data pipelines and analytical datasets. Test accuracy, completeness, integrity, schema consistency, business rules, and freshness using Python, PySpark, SQL, Spark, Airflow, and AWS. Investigate data anomalies and pipeline failures, monitor quality metrics, support CI/CD and releases, and collaborate with engineers, analysts, data scientists, and stakeholders to ensure reliable financial and regulatory data.

Develops scalable data models, Databricks pipelines, backend applications, ETL processes, and data ingestion systems. Partners with engineering, product, and business stakeholders on technical roadmaps; monitors and optimizes data systems for performance, reliability, and scalability. Provides technical leadership, mentors junior engineers, conducts code reviews, and builds secure solutions for sensitive healthcare data.

Build and operate Figma’s shared data platform supporting analytics, data science, and AI products. Responsibilities include improving development, testing, CI/CD, deployment, monitoring, data quality, incident response, performance, governance, lineage, and self-service data pipeline workflows. The role partners cross-functionally to establish reliable patterns for batch, micro-batch, reverse ETL, and product-facing data delivery.

Design, build, and operate scalable Databricks data pipelines and lakehouse solutions for observability, infrastructure, and security data. Develop PySpark and Structured Streaming workloads, optimize performance, establish data quality and governance standards, and support reliable enterprise data products. Collaborate with security, infrastructure, observability, and platform teams on data source onboarding, CI/CD, monitoring, access controls, and platform optimization.

Lead end-to-end development and deployment of enterprise generative AI and LLM applications, including agentic workflows, MCP-based data integrations, secure tool usage, AI gateways, identity propagation, access controls, and observability. The role partners with Information Security and corporate functions to deliver governed AI automation across procurement, supply chain, legal, finance, HR, and manufacturing.

The Data Test Engineer provides quality assurance for lending data microservices, daily data jobs, APIs, and business intelligence reporting. Responsibilities include functional, end-to-end, API, and data testing; issue triage; test automation; system validation; and collaboration with engineering and business teams. The role also establishes testing standards, tools, coding guidelines, and team processes while supporting technical initiatives and mentoring team members.

Create and quality-assure high-fidelity mechanical engineering datasets for AI model training, evaluation, and customer delivery. Build CAD reference models, assemblies, annotations, exploded views, sequences, and manufacturing artifacts. Develop data workflows, metadata standards, quality checks, and documentation while collaborating with product, research, engineering, and customer-facing teams. Translate mechanical engineering, CAD, manufacturing, DFM, and customer data requirements into structured datasets and scalable processes.

Build and optimize edge and cloud data pipelines for perception ML models. Scale data engine for high-fidelity labels, reduce annotation costs, integrate foundation models for automated labeling and QA, leverage software/hardware-in-the-loop testing, and support DoD field use cases and model deployment lifecycle.

Designs, builds, optimizes, and maintains data pipelines, ETL/ELT workflows, data platforms, and data models. Supports high-volume data processing, cloud and hybrid modernization, data quality, metadata, lineage, governance, and analytics-ready datasets for federal mission and AI/ML applications. Collaborates with data scientists, architects, and analysts while producing technical documentation. The role may be filled across junior, mid-level, senior, or principal career levels.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.

Owns backend software engineering for a data platform supporting federal disability claims. Designs APIs, internal tools, shared Python libraries, and services; establishes architecture, testing, packaging, documentation, and code-quality standards. Partners with data engineers, leads reviews, resolves complex defects, mentors engineers, and supports planning. Requires substantial Python, pytest, API, software design, and mentoring experience, along with U.S. citizenship and Public Trust eligibility.

Build and maintain reliable data pipelines, APIs, integrations, and event-driven or batch processing systems. Transform data from advertising, ecommerce, order management, and event sources while improving monitoring, freshness, performance, and scalability. Collaborate with data engineering, infrastructure, product, and business teams to extend the integration platform in a cloud-native, multi-tenant environment.

Own the architecture and technical direction of Northbeam’s data ingestion and integration platform. Design scalable API-based ETL, event-driven and batch processing systems; establish standards for observability, freshness, and failure handling; and guide build-versus-buy decisions. Collaborate across engineering, infrastructure, product, and business teams while mentoring engineers, leading design reviews, and driving complex platform initiatives to completion.

Build and operate AI-first full-stack applications, internal tools, service connectors, automations, and data products supporting business functions. Translate operational and financial reconciliation needs into scalable software solutions, integrate business systems, improve engineering processes, and own products across the full lifecycle. The role requires production engineering experience, data warehouse and SQL expertise, and familiarity with frontier AI tooling, with opportunities to evaluate LLM systems and work with platforms such as Snowflake, dbt, and enterprise business applications.