Top Data Engineer Jobs in Portugal

Design, build, and operate the data platform for autonomous factories: ingestion, CDC, streaming, lakehouse storage, orchestration, transformations, contracts, quality, lineage, and observability to supply analytics, ML, and operations teams.

Design, develop, and maintain Customer Data Platform solutions and data pipelines to create unified customer profiles, manage identity resolution, enable audience segmentation and activation, ensure data quality and governance, and support integrations with marketing, CRM, and cloud data systems.

Design, build, test, deploy, and support enterprise data solutions for financial reporting, reserving, and claims. Develop and maintain pipelines, transformations, data models, and controls using SQL, Python, PySpark, and Microsoft Fabric. Automate reconciliations and data quality checks, partner with business stakeholders, perform testing, review code, mentor peers, and resolve production issues to improve reporting accuracy and operational efficiency.

Provide technical leadership to design, develop, integrate, and support enterprise customer-data solutions using Salesforce Data Cloud and related cloud, API, and streaming technologies. Mentor junior engineers, collaborate with stakeholders, perform root cause analysis, and document solutions across front-end, back-end, and data layers.

Build and maintain data and model layers for an AI decision‑support system: data ingestion, probabilistic record matching, calibrated scoring, provenance‑tracked retrieval and grounded generation, and secure production pipelines. Ensure explainability, auditability, and accredited deployment in a controlled government cloud environment.

Design, build, and maintain scalable ETL/ELT pipelines and cloud-native analytics infrastructure using Python, PySpark, and SQL. Orchestrate workflows (Airflow/Databricks/Step Functions), implement CI/CD, deploy infrastructure-as-code, ensure data quality, lineage, and regulatory compliance for financial datasets, and collaborate with cross-functional teams to support reporting, risk, and downstream ML use cases.

Design, build, and maintain scalable data pipelines and processing workflows (Spark, Hive, Databricks, Airflow). Develop APIs, dashboards (QuickSight), ensure data quality, security, and performance optimizations. Write tests, perform code reviews, collaborate on CI/CD and IaC, and support data governance and integration across cloud/SaaS sources.

Design and build data and model layers for an accredited AI decision-support system: record matching, calibrated scoring, provenance-tracked retrieval and generation, secure ingestion pipelines, model packaging/serving, and instrumentation for audit and accreditation.

Lead design and delivery of scalable big-data platforms and ETL workflows; mentor engineering teams; align technical strategy with business and regulatory requirements; ensure data governance, security, and operational excellence for risk and reporting solutions.

Technical leader for Data Engineering responsible for designing, operating, and optimizing Snowflake-based data platforms, self-hosted orchestration (Prefect), dbt practices, AWS infrastructure, and data pipelines. Drive architecture, mentor engineers, partner cross-functionally, ensure observability and production-grade practices, and enable model-ready datasets and AI/LLM workflows.

Lead technical design, development, and support of scalable Oracle-based data solutions for Annuity and Life Insurance applications. Drive application modernization, batch processing with Control-M, data integration, performance tuning, production support, automation, and AI-assisted engineering practices while mentoring teams and ensuring operational excellence.

Design, build, operate, and optimize large-scale production data pipelines and foundational Healthcare Map data products using Python, SQL, Airflow, Spark, and AWS. Ensure data quality, observability, and performance; debug complex workflows; contribute to architecture, CI/CD, testing, documentation, and cross-functional delivery to enable analytics and AI/ML use cases.

Work with clients to analyze structured and unstructured data, develop predictive models and NLP solutions, build data pipelines and visualizations, guide teammates, integrate enterprise systems, and translate insights into actionable recommendations. Requires TS/SCI clearance and a Bachelor’s degree.

Design, build, and maintain on-premises scalable data pipelines and processing systems. Own architecture and technical direction, mentor engineers, optimize workflows using Python, Spark and Hadoop (Hive, HDFS, Impala), manage job orchestration with CA7/Control-M, ensure data quality, troubleshoot production issues, and collaborate with stakeholders to support analytics and AI/ML initiatives.

Define and lead enterprise data platform architecture across ingestion, storage, processing, governance, and consumption. Architect lakehouse/warehouse/streaming patterns, integrate governance/catalog tools, define security and data-product standards, drive cost optimization, high availability, and mentor engineers while partnering with ML, BI, and business stakeholders.

The AV Safety Data Reporting Engineer will support safety data reporting and compliance by analyzing large datasets and improving reporting processes across teams.

Design, build, and scale the data platform that converts raw mass-spectrometry and biochemical data into validated, labeled datasets. Own pipelines, data contracts, serving, cost optimization, quality gates, SDKs and interfaces, and operational SLAs. Collaborate with ML researchers, scientists, and product to enable model development and product features.

Own and improve annotation systems, workflows, and tooling to produce high-quality training data. Build Python and web tools, integrate model-assisted labeling and active learning, implement data quality checks, support dataset versioning and evaluation, and coordinate cross-team efforts to measure and improve annotation throughput and model impact.

Manage a team of data center engineers to ensure safe, efficient operations and 100% uptime. Oversee HVAC, electrical, controls, preventative maintenance, incident response, CMMS work orders, compliance, training, and after-hours support.

Oversee operation and maintenance of mission-critical data center facilities; supervise and mentor technical staff; monitor power, cooling, and network systems; implement preventive maintenance; manage incidents and RCAs; participate in capacity planning and infrastructure upgrades; ensure safety, security, and regulatory compliance; provide 24x7 after-hours electrical distribution support.

Build, monitor, and maintain data ingestion pipelines and databases (Databricks, Snowflake, SQL Server). Automate collection and monitoring with scripts, administer managed file transfers, create CI/CD and monitoring/alerting, diagnose incidents, and document/runbook operational procedures.

Design, build, and optimize scalable batch and real-time data pipelines and services for Target's finance systems. Work with big data and cloud platforms to deliver high-quality data for analytics and reporting, enforce governance and security, build low-latency APIs, evaluate emerging technologies, and mentor junior engineers to improve platform reliability and performance.

Design and build scalable data lakes, warehouses, and lakehouses. Implement Python ETL/ELT pipelines and Airflow orchestration, ingest from third-party APIs, optimize Parquet/Avro/ORC columnar storage, and work with Spark, Snowflake, and cloud platforms. Act as technical consultant to translate business goals into data architecture roadmaps, use Terraform for infra, and support AI/ML initiatives while ensuring data integrity and pipeline performance.

Design and build scalable data lakes, warehouses, and lakehouse architectures. Implement Python ETL/ELT pipelines, orchestrate workflows with Airflow, ingest data from third-party APIs, optimize columnar formats and SQL, support ML initiatives, consult with stakeholders, and provision infrastructure using AWS and Terraform.

Design and build scalable data lakes, warehouses, and lakehouse architectures; implement Python ETL/ELT pipelines and Airflow orchestration; ingest data from third-party APIs; manage columnar file formats (Parquet/Avro/ORC); optimize pipelines for Snowflake, Spark, and AWS; consult stakeholders to translate business goals into technical roadmaps; use Terraform for infrastructure.