Top Data Engineer Jobs in Seville

Design, develop, and maintain cloud data pipelines and Lakehouse architectures using Azure Databricks, PySpark, Delta Lake, and Azure Data Factory. Integrate multiple data sources with SQL Server/SSIS, optimize large-scale processing for performance and scalability, and collaborate with BI teams to support reporting and analytics.

As a Senior Software Engineer, you will lead backend development for the Merchant Data Platform, mentor junior engineers, ensure system reliability, and collaborate in product development. Your role involves project management, technical planning, and maintaining high availability of services.

Build and maintain pipelines that process petabytes of raw and semi-raw data, transform large-scale datasets about domains and brands, implement analytics for marketers and SEO analysts, and deploy/manage production systems using Go, ClickHouse, and CI/CD practices.

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.

Design and maintain a governed, canonical customer dataset by unifying GTM sources, building enrichment and entity-resolution pipelines, preparing structured and unstructured data for LLM/embedding workflows, implementing monitoring and lineage, and defining standards for AI-ready data across RevTech.

Design, build, and operate scalable Databricks data pipelines and Lakehouse data models using SQL, Python, and PySpark. Implement ELT/ETL and streaming patterns, data quality checks, Unity Catalog governance, performance optimization, monitoring, documentation, and operational support.

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

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.

Design, build and optimize Azure-based ETL/data pipelines and analytics infrastructure (ADF, Synapse, Data Lake/Storage). Implement scalable data solutions, automate processes, support data delivery, and create analytical tools. Collaborate with cloud, governance, and data stakeholders to deploy and maintain data solutions.

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.

Build and maintain scalable data pipelines, transformations, and analytics-ready models using dbt, SQL, Python, and Snowflake. Implement ingestion, CDC, API integrations, testing, monitoring, and observability. Collaborate with analysts and stakeholders to define metrics, ensure data governance and compliance, and optimize data platform reliability.

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.

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

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

The role involves finding audio data sources, operating and extending cloud infrastructure, and collaborating to enhance data quality and models.

Design and build data pipelines, acquire and store data, transform data into insights, optimize systems, and ensure data quality while 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 scale ETL/ELT and real-time data pipelines for AI workloads (RAG, fine-tuning, batch inference). Transform unstructured data into vectorized formats, manage feature stores and vector databases, enforce data quality/governance, integrate event systems (Kafka), and collaborate with ML and engineering teams.

As a Senior Software Engineer, you'll design and build scalable market data systems, improve API architecture, and enhance overall system performance, contributing to Alpaca's infrastructure for trading financial assets.

Seeking a Senior Data Support Engineer with 5-6 years of experience in Data Engineering, specifically utilizing Snowflake, Python, SQL, and AWS for data manipulation, warehousing, and testing processes.

The Senior Data Engineer will lead the design and evolution of scalable data architectures, mentor team members, build data pipelines, and integrate Generative AI into workflows.

Build and operate large-scale data pipelines and orchestration for generative AI research, focusing on video/audio datasets, feature extraction, labeling, and dataset quality to improve model performance.

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, build, and maintain end-to-end scalable data pipelines and Snowflake-native warehouses; optimize SQL and performance; implement Azure-based data platform solutions; develop Power BI semantic models; enable AI/ML data workflows; enforce governance, CI/CD, and security; collaborate with stakeholders and mentor engineers.