Top Data Engineer Jobs in Portugal

Develop and maintain data engineering solutions using Informatica Data Management Cloud, Control-M, Snowflake, and SQL. Responsibilities include sourcing, transforming, cleansing, ingesting, analyzing, and visualizing data; building automated workflows and scalable ETL processes; supporting data architecture and deployments; and delivering reusable code for large datasets. The role requires collaboration with technical and business partners, troubleshooting, mentoring, and managing multiple priorities.

Build and maintain scalable data pipelines and platforms for structured and unstructured data. Develop ETL and ELT workflows, convert data into usable formats, and support platform monitoring and operations. Collaborate with analysts, engineers, developers, and data consumers in an agile environment while applying analytical exploration and data examination to mission-driven client projects.

Leads the design, development, governance, and operational support of enterprise data engineering solutions for Fresh Meats analytics. Builds cloud-based data lakes, pipelines, models, integrations, and reporting-ready datasets; implements security, data quality, lineage, observability, and governance controls; supports production incidents and releases; and partners with business, platform, and governance teams. Mentors engineers and analysts while ensuring scalable, documented, secure, and reliable data products.

Build and maintain scalable data platforms, warehouses, lakehouses, ETL pipelines, and Spark-based transformations using Databricks, Snowflake, and AWS. Develop tracking architecture, data models, governance, lineage, monitoring, and optimization frameworks. Integrate machine learning and AI-assisted development practices, support analytics and KPI dashboards, and provide data access and insights across departments. Mentor engineers and analysts while coordinating with software engineering, product, DevOps, DBA, and security teams.

Hands-on principal data engineer defining enterprise-scale data architecture, building and optimizing data pipelines/integrations for analytics and ML, deploying production code, mentoring engineers, and partnering with product and ML teams to transform and analyze data for actionable insights.

Design, implement, and optimize large-scale data processing on Databricks and Spark. Build ETL pipelines, integrate data across AWS services (Redshift, RDS), orchestrate workflows with Airflow, and ensure data warehousing and engineering best practices. Lead and mentor development teams and collaborate with cross-functional stakeholders to drive data strategy and execution.

Develops, integrates, tests, deploys, secures, and sustains enterprise software and data solutions supporting Air Education and Training Command. Responsibilities include application development, APIs, ETL pipelines, data quality, Palantir Foundry and Envision configuration, testing, Agile delivery, deployment support, incident management, documentation, and stakeholder engagement. The role requires translating government operational needs into technical solutions and supporting secure data-driven applications across multiple systems.

Develops and delivers operational and analytical data solutions within an enterprise data warehouse and business intelligence environment. Responsibilities include building data pipelines, creating logical data models and mappings, supporting requirements through implementation, testing and maintenance, partnering with analysts and IT teams, and investigating data quality issues. The role requires experience with programming, cloud platforms, data warehouses, databases, ETL tools, and Windows/Linux systems.

Design, develop, test, deploy, and optimize high-throughput streaming, event-driven, and batch data pipelines using Databricks, Scala/PySpark, and cloud infrastructure. Build data lakehouse solutions, troubleshoot DevOps and Azure issues, perform root-cause and impact analysis, and support the full software development lifecycle. Collaborate with distributed stakeholders, drive technical improvements through proofs of concept, and maintain scalable data integration and curation solutions.

Design and scale event-driven data platforms using Apache Kafka and Temporal. Build batch and streaming ETL/ELT pipelines with Python, SQL, and Spark; develop data schemas, validation, contracts, and quality controls; optimize warehouse and lakehouse models; and implement reliable, observable workflows. Collaborate with software, machine learning, and analytics teams on distributed systems, CI/CD, and production data governance.

Designs and maintains operational data infrastructure for smart manufacturing, including pipelines, data warehouses, ETL/ELT processes, time-series data systems, integrations, governance, and trusted datasets. Uses SQL and Python to support reporting, analytics, automation, dashboards, and operational decision-making. Partners across manufacturing and enterprise systems, investigates complex data challenges, evaluates emerging technologies, and supports advanced analytics and digital transformation initiatives.

Design, build, and maintain scalable cloud data pipelines, datasets, ingestion frameworks, and automation for cloud cost visibility and usage insights. The role uses AWS, Snowflake, SQL, Python, orchestration tools, Terraform, and CI/CD while ensuring data quality, auditability, reliability, and performance. Responsibilities include query and cost optimization, REST API integrations, production troubleshooting, and collaboration with Product, Engineering, and Finance stakeholders. This is a remote LATAM contract requiring a U.S. EST or CST work schedule through March 2027.

Owns dbt-based transformation, dimensional modeling, and analytical data infrastructure across finance and land initiatives. Builds scalable AWS data pipelines, optimizes Snowflake storage and retrieval, develops SQL and Python solutions, and collaborates with stakeholders, data scientists, and BI engineers. Responsibilities include ETL, data warehousing, reporting infrastructure, cloud resource management, workflow automation, data security, CI/CD, and support for emerging agentic data products and MCP-based access.

Develop, maintain, and optimize scalable data solutions, enterprise ETL processes, and operational data workflows. Design, build, test, automate, and maintain data management systems; develop algorithms, predictive models, and proof-of-concepts; and collaborate with architects, modelers, and IT teams. The role also includes managing projects through delivery, supporting business initiatives, evaluating data solutions, and contributing to internal consulting and digital transformation activities.

Develops and supports ETL workflows, enterprise data warehouses, data marts, and automated data pipelines. Uses Informatica, PL/SQL, and scripting to move data between on-premises systems, Snowflake, Salesforce, and other platforms. Participates in technical design, programming, testing, standards, change control, and 24-hour on-call support. The role requires four days onsite in Lansing, Michigan and may involve ADP I or II clearance and U.S. citizenship.

Designs and builds ETL/ELT pipelines and data architectures across hybrid big-data and traditional database environments. Develops dimensional and normalized data models, ingests structured and unstructured sources, maintains data quality and security, troubleshoots pipeline issues, and delivers analytical reports and dashboards using Cognos and Tableau. Collaborates in agile teams, manages change requests, audits data integrity, and applies testing, documentation, and development best practices.

Builds and maintains ETL/ELT data pipelines and architecture for analytics. Responsibilities include monitoring jobs, troubleshooting loads, managing tickets, discovering and ingesting data from databases, APIs, files, and third-party applications, and transforming structured and unstructured data. The role also performs data modeling, quality audits, software testing, documentation, and agile project delivery while collaborating with technical teams and stakeholders.

Supports data integration development and validation by analyzing ETL processes, verifying data integrity, testing integrations, diagnosing defects, and aligning business requirements with technical solutions. Collaborates with integration developers using Agile methods, follows data security standards for personally identifiable information, and participates in requirements reviews and project planning.

Design and lead scalable, secure, highly available database infrastructure for multi-terabyte SQL Server environments. Set technical direction for data architecture, performance, real-time pipelines, security, and growth. Troubleshoot complex production issues, optimize queries and indexes, guide cross-functional projects, mentor engineers, review changes, reduce technical debt, and evaluate emerging technologies. Monitor database health using Splunk, Datadog, and Grafana while influencing engineering and Site Reliability strategy.

Build and support knowledge graph and data engineering solutions for classified defense and intelligence customers. Work with graph theory, data modeling, graph databases, and query languages to structure complex relationships. Collaborate with software development teams, communicate with analysts and senior stakeholders, and potentially develop training materials. The role requires working onsite in a SCIF and maintaining an active TS/SCI clearance while obtaining a CI polygraph.

Designs and maintains scalable data architectures, batch and real-time ETL/ELT pipelines, data models, storage systems, and governance controls. Optimizes data platforms for performance, reliability, scalability, and cost. Collaborates with architects, analysts, BI specialists, data scientists, and business stakeholders to deliver trusted datasets and technical solutions. The role also evaluates data technologies and supports cloud-based infrastructure, CI/CD, and machine learning data requirements.

Leads large-scale migration from legacy systems to cloud platforms, including data mapping, transformation, validation, cleansing, and reconciliation. Builds MuleSoft APIs and DataWeave transformations for real-time and batch integrations. Supports Salesforce Data 360, document AI, identity resolution, data governance, metadata, lineage, compliance, testing, cutover, and audit readiness in Agile, federally regulated environments.

Owns end-to-end delivery of production data platforms for utility clients, including discovery, architecture, ingestion, transformation, orchestration, governance, quality, deployment, and handover. Uses Databricks, Spark, Python, SQL, and AWS to modernize legacy systems, integrate utility data sources, build observable pipelines, and deliver operator-facing applications. Leads client engagements, manages scope and timelines, supports migrations, and contributes reusable engineering frameworks and reference architectures.

Designs and operates petabyte-scale data pipelines supporting AI training, evaluation, and continual improvement. Responsibilities include multimodal data ingestion, cleaning, deduplication, labeling, dataset versioning, lineage, quality assurance, privacy enforcement, high-throughput data delivery, storage optimization, and observability. The role collaborates with ML researchers and engineers to improve model training efficiency, data quality, reproducibility, and infrastructure performance.

Configure and maintain Adobe Experience Platform and Real-Time CDP data pipelines, XDM schemas, datasets, identity resolution, Profile enablement, segmentation readiness, and destination activation. Validate data quality, troubleshoot ingestion and identity issues, manage sandboxes, and implement privacy, consent, governance, and access controls. Partner with architects, data engineers, consultants, analysts, data scientists, and marketing teams to support reporting, personalization, and machine learning readiness.