Top Data Engineer Jobs in Valencia

The Data Engineer II will develop and optimize data platforms, build scalable pipelines, enhance existing workflows, and collaborate with teams for data solutions.

Own and scale secure cloud infrastructure for data and AI platforms across AWS and GCP. Lead Terraform-based infrastructure as code, reliability and observability programs, GitLab CI/CD automation, Docker and Fargate deployments, and platform enablement for Airflow, dbt, streaming, and AI workloads. Establish security, access-control, cost-governance, monitoring, alerting, and SLO practices while partnering with data engineering teams.

Owns and scales secure cloud infrastructure for data and AI platforms across AWS and GCP. Responsibilities include Terraform-based infrastructure as code, platform reliability and observability, SLI/SLO frameworks, GitLab CI/CD automation, Docker and Fargate orchestration, and secure enablement of Airflow, dbt, streaming, and AI workloads. The role also manages access policies, network security, and cloud cost optimization while partnering with data engineering teams in a hybrid Valencia environment.

Design, build, and operate reliable, observable, and scalable data and AI infrastructure. Implement IaC with Terraform, CI/CD automation, data observability, streaming pipelines (Beam/Flink/PubSub), feature stores, and security/governance to support Data Science and production AI/ML workloads.

The Senior Data Engineer designs and implements data infrastructure, oversees data pipelines, and ensures data reliability while mentoring junior staff. Responsibilities include automating governance, optimizing data performance, and collaborating with various stakeholders.

Build the companys first data stack and data products (internal analytics and customer-facing). Work across data engineering, devops, and software tasks; ship early features, improve developer experience, document proposals, and iterate based on feedback.

Design, build, and operate enterprise reporting and analytics platforms. Create scalable ELT/ETL pipelines, master data foundations, and lakehouse/warehouse semantic models. Deliver Power BI datasets and dashboards, implement data quality, governance, DevOps practices, and cost-management data solutions. Partner with finance and stakeholders and provide technical leadership across architecture, deployment, and run-state support.

Build and own end-to-end data foundations, including ingestion pipelines, dbt models, data marts, semantic layers, and AI-ready datasets. Integrate fragmented marketing, advertising, ecommerce, analytics, and CRM data across a multi-tenant Snowflake environment. Manage platform API changes, data quality, restatements, performance, and client-specific modeling. Collaborate with product, engineering, AI, BI, and client teams while using AI coding agents to accelerate reliable production development.

Design, build, and maintain cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake and Azure Data Factory. Integrate multiple data sources, optimize performance for large datasets, support SQL Server/SSIS environments, and collaborate with BI teams (Power BI) to enable analytics and reporting.

Build scalable data architectures and pipelines for API, batch, event, and streaming sources. Develop aggregation, preprocessing, governance, security, and lifecycle processes; support cloud OLTP and OLAP workloads; and integrate machine learning and analytics components, including MLOps. Collaborate with data science and application development teams to create, deploy, monitor, and maintain secure data solutions using AWS, containerization, infrastructure as code, and CI/CD.

The Senior Data Engineer develops data solutions, builds scalable data pipelines, and fosters best practices while collaborating within a multidisciplinary team.

Lead design and evolution of the data platform: architecture, schema design, governance, and infrastructure on AWS using Terraform. Ensure AI-ready, high-quality data; set standards, mentor engineers, and drive cross-team technical direction.

Design and maintain ETL/ELT pipelines on Google Cloud Platform, optimize data processing workflows, and collaborate with stakeholders to develop data solutions.

The Renewables Measurement & Data Engineer will validate renewable measurement data, support on-site measurements, troubleshoot systems, and analyze resource potential and asset performance.

The Data Engineer will design, build, and optimize data solutions, develop data pipelines, and work with stakeholders in BI and analytics environments.

Design and lead scalable, secure data architectures and end-to-end data flows across cloud and hybrid environments. Define standards, guide pipeline development and modeling, advise on storage, governance and performance, evaluate new tools, and lead technical initiatives across teams.

Own end-to-end AI-automated data platform migration projects (1-4 concurrently): scope, plan, execute, and hand off. Act as primary customer contact, configure Datafold's Migration Agent, partner with engineering on execution, and help refine delivery playbooks.

Owner of marketing-focused data engineering: build and maintain integrations, tracking, and pipelines across BigQuery, ClickHouse, GCS, and external systems; troubleshoot tracking, payment, and data quality; collaborate with cross-functional teams and document data flows.

The Principal Data Engineer will improve data pipeline architecture, optimize data flows, and support data projects for various teams in the company.

Build and maintain high-volume data processing jobs (Redshift, Spark, Athena, Iceberg, Airflow). Collaborate with data scientists and analysts to productionize models, identify performance bottlenecks, and mentor junior engineers. Support data modeling, warehouse management, and troubleshooting.

Design, build, and maintain scalable ELT data pipelines with Airflow; develop dbt data models and tests; optimize Athena tables/queries and data workflows; provision infrastructure with Terraform; ensure data quality and reliability; collaborate with Analytics, BI, and Engineering; troubleshoot pipeline and data quality issues.

The Software Engineer will focus on data collection and infrastructure for AI model training, collaborating with scientists to enhance data acquisition processes.

Design and implement Azure-based data infrastructure: build ETL/ELT pipelines, data storage (Data Lake/Warehouse), optimize data delivery, develop analytics and tooling, and collaborate with stakeholders to deploy and support data solutions.

Design and implement Azure-based data infrastructure and pipelines (hub-and-spoke). Build ETL/ELT processes, data storage (Data Lake/Warehouse), analytics tools, collaborate with stakeholders, and optimize data delivery and automation.

Design and build scalable Azure-based ETL pipelines, data storage and analytics solutions. Collaborate with stakeholders to optimize data delivery, automate processes, support infrastructure, and create analytical tools to surface business insights.