Top Data Engineer Jobs in Portugal

The role involves building and optimizing big data pipelines, designing data architectures, collaborating with data scientists, and driving technical strategy for data processing.

As a Software Engineer, you will enhance the data infrastructure by building new concurrency controls, indexing schemes, and optimizing distributed data processing algorithms.

Develop and maintain real-time market data feeds, focusing on performance and latency improvements in a collaborative environment.

You will create large datasets and training recipes, develop methods for data mining, create scalable infrastructure solutions, and collaborate with ML infrastructure teams.

Own and develop concurrent C++ backend services for Webviz to stream time-series and sensor data, integrate offboard storage and WebRTC, optimize latency and throughput, build APIs for automated triage/evaluation, plan technical roadmaps, and mentor engineers.

As a Software Engineer, you will create tools and pipelines for AI development, manage data solutions for ML teams, and ensure high-quality code.

Serve as the technical lead for Acceldata's Hadoop observability and open data platform in pre-sales: deliver demos, design solutions, run POCs, craft technical proposals/SOWs, collaborate with sales/product/engineering, and support closing deals while educating internal teams and producing technical documentation.

As a Staff Software Engineer, you will build distributed data systems, focusing on reliability and performance for large-scale data processing, using technologies like Apache Spark™ and Delta Lake.

Build next generation distributed data storage and processing systems, including projects like Apache Spark and Delta Lake, while focusing on customer value and handling complex data engineering pipelines.

As a Senior Software Engineer at Databricks, you will design and build advanced distributed data storage and processing systems focusing on optimizing performance and supporting various data workloads.

Design, implement, and maintain automated data testing and validation frameworks to ensure data integrity across ETL/ELT pipelines and reporting. Develop reconciliation, profiling, and automated test scripts; embed quality controls into Snowflake, Oracle, and AWS pipelines; support test environment strategy and governance; collaborate with engineers, QA, and stakeholders to improve DataOps, monitoring, and reporting validation while mentoring junior team members.

Seeking a Senior Staff Engineer specializing in Big Data to lead teams, tackle client challenges, and drive innovative solutions. Must mentor teammates and contribute to dynamic projects.

Design, develop, and test datacenter/SDN networking solutions focused on Layer 3 protocols. Produce architecture and design artifacts, support implementation and transition, automate testing, and resolve SDLC issues while coordinating with stakeholders.

Lead enterprise data architecture and platform engineering efforts using Databricks Lakehouse. Design scalable cloud-native data platforms, implement medallion architectures, enable data governance and AI/ML workloads, build automation and CI/CD for Databricks, and mentor teams while collaborating with stakeholders on strategy and delivery.

Design, build, and maintain production data pipelines and infrastructure for robot telemetry, logs, sensor data, and events. Implement ingestion, transformation, and orchestration (Airflow), backend services/APIs, and AWS-based storage/processing. Improve reliability with monitoring, retries, schema validation, and data quality checks. Collaborate with robotics, ML, and deployment teams to scale data systems for fleet operations.

Build end-to-end data and analytics features: ingest and model data, build transformations and pipelines, create client-facing dashboards and interfaces, and develop LLM-powered workflows to accelerate analysis and app development. Partner with stakeholders, perform exploratory analyses, and ensure data quality, observability, and maintainability while influencing data engineering strategy.

The Application Engineer will develop analytics using Pig and PySpark, manage data flow, ensure process reliability, and collaborate on data projects.

Own end-to-end data migrations from messy legacy municipal systems into GovWell: map and ingest data, validate and reconcile with SQL/Python, lead technical onboarding calls with government staff, ensure production data quality, and iterate on tools and playbooks with Product and Engineering.

Lead architecture, implementation, and optimization of scalable data platforms, ETL/ELT pipelines, and data governance. Ensure security and compliance, mentor junior engineers, drive CI/CD and IaC for data workflows, and collaborate with stakeholders to deliver actionable analytics and reporting.

Design and build enterprise data warehouse, scalable data pipelines, integrations (OneStream, Workday, Oracle, Databricks), and AI/agent solutions (RAG, vector stores). Partner with Finance/HR/Legal/IT to translate workflows into automated, secure, maintainable enterprise data and AI services, and support analytics and reporting.

Design, build, and maintain scalable distributed data pipelines and a secure data lakehouse for streaming and batch processing to support real-time analytics, ML, and experimentation. Automate, test, and harden workflows, architect logical and physical data models, build ML model features, and collaborate with product, analytics, and data science teams to turn data into value.

Design, build, and operate a scalable, secure cloud data platform (Lakehouse) using Databricks/Snowflake on Azure/AWS. Implement IaC, CI/CD, automation, governance, monitoring, cost optimization, and self-service tooling while partnering with architects, data teams, and security to support analytics and AI workloads.

Develop, test, and maintain big data applications using Hadoop ecosystem (Hortonworks), Spark, Scala, Hive and MapReduce. Improve and optimize existing code, collaborate with analysts and testers, and apply Agile practices within distributed, component-based architectures to deliver reliable data solutions.

Develop and enhance a retail engagement analytics product by implementing data engineering solutions: build real-time data flows, work with NoSQL stores, support data infrastructure and visualization, clean and prep noisy datasets, and recommend improvements to data reliability, efficiency, and quality.

Technical lead for data engineering who designs, builds, and owns scalable Snowflake ELT pipelines and data models. Mentors engineers, drives standards, optimizes performance and costs, and partners with product, analytics, QA, and governance to deliver observable, high-quality data solutions. Contributes to platform strategy, DataOps/CI/CD adoption, and ensures data quality, testing, and documentation.