Top Data Engineer Jobs in Portugal

Build and maintain Terraform-managed Databricks data platforms on AWS, develop secure external data-sharing capabilities, implement data governance controls, automate audit evidence collection, and improve platform reliability, performance, monitoring, and cost efficiency. The role also involves incident resolution, on-call participation, data pipeline development, compliance collaboration, and designing secure-by-default infrastructure for publisher partners.

Architects, deploys, and operates scalable data platform infrastructure supporting analytics. Responsibilities include administering Airflow and Superset, managing Terraform infrastructure, building CI/CD pipelines, implementing security and compliance controls, developing dbt data models, cataloging assets in OpenMetadata, automating with Python, and mentoring engineers. The role requires extensive infrastructure and data engineering experience, cloud expertise, container orchestration, and eligibility for CJIS clearance.

Build and maintain Databricks data pipelines and reusable models for Go product analytics. Define metrics, improve event logging, connect product usage with customer data, and support adoption and growth analysis. Ensure data quality through testing, monitoring, troubleshooting, and documentation. Optimize pipeline performance and costs while collaborating with Product, Engineering, and Data Science. Contribute dashboards and analyses that support product and business decisions.

Designs, builds, and operates scalable cloud data pipelines, platforms, and applications. Responsibilities include data modeling, distributed data processing, relational and NoSQL database development, architecture decisions, observability, streaming pipelines, and data warehousing. The role leads large-scale data initiatives, evaluates technologies such as Snowflake and Databricks, collaborates with product and engineering teams, communicates technical concepts, and mentors data engineers.

Build and lead production AI/ML, Generative AI, and data engineering solutions for healthcare analytics and patient safety platforms. Develop full-stack applications, microservices, APIs, data pipelines, dashboards, self-service analytics, RAG systems, and event-driven architectures using cloud-native technologies. Apply engineering best practices including testing, CI/CD, observability, security, and performance optimization. Mentor engineers, lead technical initiatives, and collaborate with product, analytics, business, and platform teams.

Build and scale data pipelines, APIs, integrations, and data models supporting CRM personalization, segmentation, audience activation, and campaign measurement. Ensure pipeline reliability through monitoring, alerting, and logging; automate workflows; document systems; participate in architecture and code reviews; and mentor junior engineers. Collaborate with data engineering, data science, and CRM teams to improve personalized email, push, and SMS engagement for millions of users.

Designs and maintains scalable data pipelines and platform architecture using Python, dbt, Airflow, and Snowflake. Responsibilities include data modeling, ELT development, quality and observability, production debugging, source-data validation, performance optimization, and Tableau reporting. The role leads ad hoc analysis, collaborates with engineering, marketing, product, and leadership teams, makes architectural decisions, and mentors colleagues through code reviews and knowledge sharing.

Build and maintain scalable data pipelines, analytical data models, backend data systems, business rules, metrics, and data sources. Partner with teams across finance, growth, product, customer support, sales, and marketing to support analytical needs and improve data-driven decision-making. Own data engineering problems end to end and help advance organizational data practices.

Data Engineering Interns will define metrics, instrument logging, ingest and model data, transform datasets, ensure data quality and governance, and support visualization, reporting, alerting, and scalable systems. They will partner with product, analytics, and data consumers to improve product engagement, efficiency, costs, and strategic decision-making. The 12-week, full-time internship includes technical training, mentorship, and hands-on project experience.

Design, build, and operate scalable cloud data platforms, pipelines, and applications using distributed data technologies. Lead architectural decisions, data modeling, platform selection, and large-scale transformation initiatives. Collaborate with product managers, software engineers, analysts, and data scientists while ensuring resilience, maintainability, and operational efficiency. Mentor engineers, establish engineering standards, communicate technical outcomes, and independently deliver end-to-end data solutions across relational, NoSQL, batch, and streaming environments.

Lead the design and development of scalable, low-latency real-time data platforms and distributed streaming pipelines. Build data solutions using Flink, Spark, Kafka, Snowflake, AWS services, and related technologies. Guide engineers, establish data architecture and pipeline best practices, develop cloud-based solutions, support Agile delivery, perform testing and code reviews, and mentor team members. The role also involves data modeling, observability, data warehousing, and collaboration with product, security, and engineering teams.

Build and operate governed Finance data infrastructure on Databricks, including scalable pipelines, integrations, semantic layers, data-quality controls, observability, and AI-enabled services. Partner with analytics and enterprise teams on architecture, data contracts, security, recoverability, and production ownership. Optimize platform costs through FinOps practices and use AI coding assistants while validating performance, correctness, and security.

Design, build, and operate scalable cloud data platforms, pipelines, and applications. The role involves distributed data processing, data modeling, relational and NoSQL databases, warehousing, streaming, security, and observability. The engineer collaborates with Agile teams, architects reusable engineering patterns, communicates technical outcomes, mentors colleagues, and delivers resilient data solutions using technologies such as Python, Spark, Databricks, and Snowflake.

Design, build, and architect scalable cloud data platforms, pipelines, and applications. Lead large-scale data initiatives, make architectural decisions, evaluate technologies such as Snowflake and Databricks, enforce engineering patterns, and ensure performance and resilience. Collaborate with product, engineering, analytics, and data science teams while mentoring engineers and communicating technical outcomes to stakeholders.

Build and architect scalable cloud data platforms, pipelines, and applications using Python, Spark, SQL, Databricks, Snowflake, and relational and NoSQL databases. Collaborate with engineering, product, analytics, and data science teams; implement secure, resilient, reusable data solutions; support distributed and streaming workloads; apply data modeling and observability practices; communicate technical concepts; and mentor other data engineers.

Build and operate scalable cloud data platforms and pipelines for Capital One’s Risk Tech organization. Responsibilities include designing distributed data solutions, enforcing engineering patterns, implementing security controls, collaborating across Agile teams, mentoring developers and analysts, and delivering AI-powered risk management products. The role requires expertise in Python, Spark, SQL, databases, cloud data warehousing, data modeling, and pipeline development.

Build, optimize, and maintain scalable data pipelines and reusable datasets for business intelligence, marketing, and data science. Develop a federated data validation framework, optimize queries and data models, and protect sensitive healthcare data through privacy and security best practices. The role uses Python, SQL, AWS, Airflow, PostgreSQL, Snowflake, and modern data-stack technologies. This position is based in New York City and requires working onsite three days per week.

Design, build, deploy, and operate production LLM-powered agents on JPMorganChase’s NEO platform. Responsibilities include Graph RAG, agent memory, context and entitlement management, multi-agent workflows, MCP integrations, evaluations, guardrails, observability, cloud deployment, security, resiliency, and collaboration with business and engineering stakeholders. The role also requires identifying data and architecture improvements across the software development lifecycle.

Build and maintain scalable data pipelines supporting reporting, analytics, and AI/ML use cases. Write production Python and SQL code, integrate data from APIs and other sources, establish data governance, implement quality monitoring and alerting, support production incidents, and document technical solutions. Collaborate with data scientists, engineers, and business partners to deliver reliable data infrastructure and insights.

Build and maintain scalable data models, pipelines, ETL workflows, and streaming solutions using dbt, PySpark, AWS Glue, Databricks, and Kafka. Apply software engineering practices including testing, version control, code reviews, and integration. Partner with data scientists, stakeholders, and data stewards to improve data quality, governance, accessibility, and reliability. Translate requirements into development plans and Jira stories while collaborating on data strategy, tooling, integrations, and deployments.

Build and maintain Databricks data pipelines using SparkSQL and PySpark; ingest, transform, model, and validate healthcare data from payer, provider, platform, and API sources. Support automated file exchanges, dashboards, downstream workflows, testing, governance, access controls, and audit logging. Participate in design and code reviews, use AI coding tools responsibly, and progressively own data engineering work while handling protected health information.

Designs, builds, and operates scalable data pipelines, services, and products supporting clickstream and behavioral data. Responsibilities include data modeling, API and system design, automated quality frameworks, observability, governance, reliability, security, and performance optimization. The role collaborates with engineering, product, analytics, and domain teams, translates complex requirements into production solutions, and applies AI/ML-enabled tools and workflows. It also contributes to technical decisions, data-driven improvements, and operational excellence.

Designs, builds, and operates scalable cloud data platforms, APIs, pipelines, and data products. Leads architectural decisions, evaluates platforms such as Snowflake and Databricks, develops distributed and streaming workloads, enforces engineering standards, and mentors data engineers. Collaborates with product managers, software engineers, analysts, and data scientists to deliver resilient solutions. Requires extensive experience with application development, distributed data, SQL, programming, data pipelines, data modeling, cloud platforms, and relational and NoSQL databases.

Develop and automate comprehensive tests for data pipelines, ETL/ELT processes, and cloud data platforms. Validate data quality, transformations, performance, scalability, and time-series biosensor outputs. Build synthetic datasets that replicate production data, identify anomalies and bottlenecks, and improve testing frameworks. Collaborate with data engineers and scientists to ensure reliable data throughout the lifecycle supporting regulated software-as-a-medical-device products.

Leads development of secure, scalable data platforms for KYC and risk assessment. Designs and delivers enterprise data applications, frameworks, APIs, and processing systems; mentors engineers; drives AI-assisted development practices; and ensures secure, reliable, and compliant engineering workflows. The role uses cloud-native technologies, large-scale data processing, databases, observability, orchestration, modern data platforms, and emerging LLM infrastructure while communicating with senior leaders.