Top Data Engineer Jobs in Portugal

Design and build scalable platforms for data ingestion and storage, collaborate with hardware engineers, and define engineering processes at Sift.

Build and maintain high-performance, fault-tolerant distributed backend services and data pipelines for large genomic and health datasets. Design storage, indexing, and async event workflows; optimize latency, throughput, and reliability. Partner with bioinformatics and product teams to validate scientific accuracy and guard AI-driven responses.

Design and implement SQL transformations and datasets, manage data models in Databricks, optimize performance, and ensure compliance with data governance standards.

As an MLOps engineer, you will develop optimized data pipelines, enhance data engine architecture, and integrate foundation models while working closely with DoD customers.

Build and maintain scalable cloud-native data platforms and pipelines for analytics. Design system architecture, ingest high-volume bounded and unbounded data, optimize performance and cost, write tests and documentation, collaborate with product and EHR specialists, and adopt AI-assisted development tools to improve delivery and quality.

Design, build, and maintain scalable, secure data architectures, models, and ETL pipelines. Integrate diverse data sources, optimize performance and data quality, implement governance, monitor production SLAs, and collaborate with data scientists, analysts, and product teams. Provide technical leadership, documentation, and training to improve engineering practices across the organization.

Create compelling visual stories from Planet's satellite imagery by analyzing geospatial data, producing cartographically sound graphics and interactive content, collaborating with marketing and media teams, and ensuring accurate representation of data under tight deadlines.

Lead design and implementation of scalable Azure-based data pipelines and architectures using ADF, Synapse, Spark/Databricks, and Power BI. Mentor engineering teams, ensure data security/compliance, collaborate with stakeholders, and drive adoption of best practices and new technologies.

Design, build, and operate high-throughput distributed data pipelines and platform infrastructure. Own end-to-end systems from architecture to production, write production-grade Python, optimize SQL at scale, architect service contracts, mentor engineers, and instrument systems for observability.

Design, build, and maintain Snowflake-based data warehouse and lakehouse pipelines using dbt and SQL. Optimize performance, ensure data quality, support CI/CD and orchestration, collaborate on cloud infrastructure, document models and runbooks, and troubleshoot pipeline issues.

Design and govern data models, pipelines and integrations to support AI and analytics for Supply Chain. Improve reuse, lineage, quality, certification and traceability. Collaborate with ICT, engineering and business stakeholders to scale enterprise data delivery and ensure operational, governed data assets.

Design, build, and maintain scalable ingestion, normalization, validation, storage, and lifecycle management of large-scale market and alternative datasets. Develop Python- and SQL-based pipelines, monitoring, and tooling to ensure research-ready, high-quality data across multiple asset classes while collaborating with researchers and infrastructure teams.

Design, build, and operate scalable, highly available database and data-serving infrastructure for production and AI workloads. Scale relational, analytical, and vector stores; optimize latency, throughput, and reliability; implement partitioning, sharding, replication, caching, query optimization, observability, backups, and disaster recovery. Partner with AI and engineering teams to support embeddings, vector search, retrieval workflows, analytics, and low-latency data access while building automation, tooling, and best practices for database operations.

Lead a team of 5-6 data engineers to design and implement scalable data pipelines (Airflow), manage Snowflake data architecture, build real-time streaming solutions with Kafka/Spark Streaming, develop and integrate APIs, enforce code quality, and drive technical decisions and best practices.

Design, build, and maintain scalable ETL/ELT pipelines and product-level data architectures. Improve pipeline performance and data freshness, lead code/design reviews, mentor engineers, and collaborate with Data Science, Product, and Engineering to make data reliable and accessible.

Own and build Snowflake-based analytics and AI-ready data products. Design and maintain dbt pipelines, semantic models, RBAC and security policies, optimize queries and warehouses, integrate data via Fivetran, create dashboards, troubleshoot production issues, document designs, enforce coding standards, and mentor junior engineers while partnering with business stakeholders.

Design and build scalable data platform services, automation, and integrations. Improve pipeline reliability, observability, schema governance, and developer self-service. Collaborate with data engineering, analytics, and DevOps to deliver APIs, tooling, and production-grade data pipelines.

Build and maintain cybersecurity data pipelines, manage SIEM and log collection, perform root cause analysis, maintain system availability and configuration management, support cloud/virtualized infrastructure, and coordinate with government customers and service providers.

Design, build, and operate production-grade near-real-time data pipelines and APIs on AWS (Kinesis, Glue/Spark, Lambda, EMR). Implement IaC and CI/CD, optimize data lakes and operational models, improve observability and reliability, and participate in on-call support.

The Senior Site Reliability Engineer is responsible for maintaining Wikimedia's infrastructure, improving reliability, automating processes, and collaborating with teams. The role involves troubleshooting, managing deployments, and leading incident responses while working remotely.

Design, develop, validate, and deploy scalable AI/ML and simulation/optimization solutions using structured and unstructured data. Lead model development within model risk frameworks, mentor junior scientists, collaborate with engineering and product teams for MLOps/cloud deployments, and communicate actionable insights to business partners while ensuring regulatory and risk compliance.

Provide application support, integration, network administration, disaster recovery, and project management. Develop C/C++ on UNIX or embedded OS, support Microsoft and Unix servers/desktops, design secure networking and gateway appliances, author functional specifications, and collaborate with architects on system design.

Design, develop, test and support Big Data Java applications across the full software lifecycle. Build reusable frameworks and Java components, collaborate with architecture and business stakeholders, lead technical decisions, and troubleshoot production issues. Work with Unix, Java, SQL, Spark, Pig, Python and related big-data tools.

Lead Engineer responsible for administering and tuning Hadoop/NoSQL ecosystems (HDFS, Hive, YARN, Spark, Impala, HBase), managing Kafka/NiFi, automating operations, implementing security (Kerberos, Sentry), supporting data lakes, cloud/on-prem integrations, and driving capacity, backup/disaster recovery and vendor evaluations.

Design, ingest, validate, and manage complex financial and annuity data from external partners. Build and maintain ETL pipelines, SQL queries, and data quality controls; investigate data anomalies; collaborate with Accounting, Actuarial, and Technology; support reporting and process automation to improve financial and regulatory workflows.