Top Data Engineer Jobs in Barcelona

The Lead Software Engineer will develop AI-powered software solutions, design backend systems, and collaborate with cross-functional teams to enhance AI capabilities.

The Software Engineer will manage data ingestion for AI model training, enhance cloud infrastructure, and collaborate on dataset development for Speechify products.

Design, build and maintain scalable data pipelines, models and infrastructure to power product analytics. Partner with BI, Data Science and product teams, implement testing/monitoring, and contribute to AI-enabled tooling and analytics platforms.

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 deliver data engineering and automation solutions for internal R&D systems. Migrate legacy data, enable Agentic AI readiness, analyze business processes, produce data-driven insights, ensure compliance/validation, and collaborate globally to standardize and automate internal tools and workflows.

Design and maintain data infrastructure and tooling, develop scalable data pipelines, ensure data quality and security, and collaborate with other teams to enable insights.

Design, build, and maintain data ingestion and transformation pipelines; manage storage and processing layers; ensure data quality, performance, and reliability; implement data engineering best practices; provide technical leadership and mentor data engineers.

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.

Build and improve data infrastructure and pipelines, integrate sources, develop analytical data models and reporting, perform ad-hoc business analyses and scenario simulations, and enable data-driven decision-making across teams.

Build and evolve a cloud-based data platform (Databricks/AWS) to support analytics and AI. Design scalable data pipelines, enable ML/AI in production, enforce governance/observability, define engineering standards, and mentor the data team while collaborating with product, engineering, and business stakeholders.

The Senior Development Engineer/Mathematician/Data Analyst will develop algorithms, create statistical models, and implement biometric methods to enhance diagnostic accuracy and support product approvals.

Lead design and build of end-to-end scalable data pipelines and data models (dimensional modeling) using SQL, dbt, Airbyte, and Airflow. Partner with stakeholders to deliver clean data for analytics and ML, mentor engineers, enforce data governance, and support performance tuning and documentation.

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.

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.

Build a greenfield data platform and unified Lakehouse integrating high-volume payment, marketing, and operational data across AWS and GCP. Design ingestion pipelines, create dbt medallion-layer transformations in BigQuery, and own Airflow orchestration, lineage, backfills, retries, alerting, infrastructure as code, and cost controls. The role requires autonomous ownership in a fully remote, asynchronous environment, with data mesh and Starburst Galaxy experience as preferred qualifications.

Lead data and analytics projects: design dimensional models, build Power BI dashboards, run exploratory analysis, capture client requirements, develop end-to-end data pipelines on Azure, test and assure solutions, support client decision-making, and contribute to business generation and people development.

Lead delivery of end-to-end data and analytics solutions: build dimensional models, develop Power BI dashboards, perform exploratory analysis, design Azure-based data pipelines, capture client requirements, run scenario modelling, test and assure solutions, prepare client communications and lead project teams.

Build and maintain Camunda 8 backend data-layer services across relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, streaming, and secondary-storage solutions; investigate production data issues; improve performance and operability; and deliver engineering initiatives collaboratively with Product, QA, Support, and SRE. Participate in L3 support and on-call rotations.

Lead mechanical due diligence and feasibility for data center sites and deals. Validate cooling topology and equipment strategy (air, liquid, hybrid), assess capacity, vendor lead times, and integration with budgets and schedules. Define mechanical readiness gates, deliver validated scope to Delivery, coordinate with stakeholders and vendors, and help build repeatable evaluation standards for AI/hyperscale infrastructure acquisitions.

Own and operate Databricks production data pipelines for a predictive CRM platform: monitoring, incident response, cost and performance optimization, migrating legacy ETL, onboarding customers, enforcing Delta Lake architecture and multi-tenant isolation, and applying CI/CD, security, and observability best practices to ensure reliable, scalable data delivery.