Top Data Engineer Jobs in Barcelona

Build and maintain high-performance data lakehouse infrastructure, including Apache Iceberg maintenance workflows, compaction, snapshot expiry, manifest rewriting, and orphan file cleanup. Develop Rust-based query routing systems, connectors, and production services while ensuring data integrity under concurrency and failures. Responsibilities include open-source collaboration, code reviews, releases, on-call support, and debugging customer environments.

Build and operate scalable GCP and BigQuery data platform infrastructure using Terraform and CI/CD. Improve security, access controls, reliability, monitoring, performance, and cloud cost efficiency. Operate Airflow and Cloud Composer, develop self-service tooling, investigate incidents, implement recovery processes, and translate governance requirements into practical controls. Partner with data, analytics, security, and governance teams on technical design, roadmap development, documentation, mentoring, and platform operations.

Build and operate batch and streaming data pipelines, data models, and AWS/Snowflake lakehouse infrastructure. Develop internal Python platform tooling, enforce data quality and governance, manage privacy workflows, optimize cost and performance, and respond to pipeline incidents. Collaborate with infrastructure, backend, advertising technology, and business teams to deliver reliable data products.

Investigate marketing tracking discrepancies, trace and fix end-to-end data pipelines (especially GA4/Salesforce and gclid issues), improve revenue attribution and reporting, and partner with marketing, engineering, and sales to implement reliable tracking and reporting solutions.

Maintain and govern data tools, automate data processes using LLMs and workflow platforms, monitor GCP usage and costs, and improve Looker and self-service data support. The role contributes to data transformations, quality validation, documentation, governance, and conversational analytics while mentoring interns and partnering with engineers and business stakeholders.

Build and optimize large-scale data pipelines, cross-game data marts, analytics tools, and AI-powered workflows. The role requires advanced SQL and Python, production experience with Airflow, Docker, S3, and CI/CD, plus collaboration with game and marketing analysts. Responsibilities include developing reusable data frameworks, maintaining end-to-end AI pipelines, supporting experimentation and model feature engineering, and mentoring analysts on coding and software engineering best practices.

Build and maintain scalable production data pipelines, machine learning data pipelines, and data transformation frameworks for financial crime detection solutions. Collaborate with product, R&D, data, analytics, customers, and partners; optimize large-scale data architectures; support feature engineering and root cause analysis; and train customer teams. The role requires customer-facing collaboration, English and Spanish fluency, and domestic and international travel when needed.

Own and grow the Data Layer: build and operate ingestion, transformation, reconciliation, and serving pipelines (raw→refined→curated). Use agentic AI tools to accelerate delivery, ensure data correctness, and partner with backend, AI, and product teams to define reliable data contracts.

Build and scale governed data products, pipelines, analytical models, semantic layers, APIs, and AI-enabled interfaces. Establish standards for data modeling, orchestration, testing, observability, CI/CD, security, and lifecycle management. Partner with stakeholders to define data contracts and product roadmaps, deliver solutions end to end, improve analytics governance, and mentor colleagues. The role requires strong data and analytics engineering expertise, product judgment, and practical experience directing AI coding agents.

Build and operate a cloud-based data platform on GCP, including Terraform-managed infrastructure, CI/CD pipelines, batch and streaming data workflows, multi-cloud migration, governance guardrails, observability, operational documentation, and cost controls. The role combines cloud platform engineering, data engineering, software development, and AI-assisted automation while supporting resilient, auditable production services.

Build and scale Azure-based data pipelines and a shared data foundation for e-commerce search and product discovery. Integrate clickstream, query logs, telemetry, and product interaction data using Medallion Architecture. Implement distributed processing, data quality checks, lineage, governance, and relevance feedback loops. Collaborate with analytics, data science, and product teams while delivering clean, tested, documented code and a functional prototype rapidly.

Staff Software Engineer responsible for designing, building, and operating distributed market data systems. The role focuses on real-time blockchain data ingestion, streaming, normalization, storage, APIs, reliability, data correctness, observability, incident recovery, and production support. The engineer will lead technical decisions, mentor teammates, translate ambiguous requirements into designs, and use AI-assisted development throughout the software lifecycle.

Develops and governs clinical data standards and study specifications aligned with CDISC and regulatory guidance. Partners with statisticians and programmers to create compliance and productivity tools, mentors teams, monitors standards and regulatory updates, supports submissions, and represents the organization in industry standards initiatives. Requires extensive programming, clinical trial data, regulatory submission, mentoring, and CDISC expertise.

Build and maintain data pipelines and infrastructure for analytics, experimentation, and company metrics. Collaborate with machine learning engineers on training, quality assessment, and model release automation. Improve data delivery processes, automate manual workflows, and develop scalable data models. The role requires strong Apache Spark and Python expertise, SQL proficiency, Git experience, and familiarity with cloud data platforms and Kubernetes.

Build and maintain data pipelines, infrastructure, data models, and automation for analytics and machine learning workflows. Collaborate with ML engineers on model training, quality assessment, and releases. Improve internal data delivery processes and enable efficient access to large-scale organizational data.

Design, develop, and support scalable data solutions, including ETL/ELT pipelines, data models, integrations, and cloud data platforms. Translate stakeholder requirements into technical designs, produce documentation, own end-to-end delivery, and collaborate across teams. The role also involves improving data platform capabilities, promoting engineering best practices, estimating and prioritizing work, and mentoring junior colleagues.

The Data Engineer will design, build, and maintain data pipelines while collaborating with teams to provide data-driven solutions and enhance data quality.

Build end-to-end data applications, APIs, internal tools, and user interfaces using AI-assisted development. Work across Python backends, React frontends, data models, Databricks, dbt, pipelines, cloud infrastructure, testing, security, and performance. Review AI-generated code, establish guardrails, improve engineering workflows, and help shape application architecture and technical direction.

Design, build, and operate high-performance backend systems for web data acquisition handling nearly 200 million daily requests. Develop proxy layers, browser tooling, request routing, and protocol-level infrastructure primarily in Rust. Investigate crawler and blocking issues, build reliable interfaces and guardrails, and maintain code quality through testing, observability, and secure scalable solutions. Collaborate with crawler engineering, DevOps, Security, and other teams.

Build and evolve CloudBlue’s high-volume data and analytics platform, including real-time ingestion, pipelines, storage, data models, APIs, and production observability. Develop AI-powered workflows and integrate AI into backend services and business processes. Ensure data quality, reliability, performance, testing, and production SLOs while collaborating with cross-functional engineering and product teams in a distributed environment.

Develop and maintain cloud-based data platforms, scalable data pipelines, ETL workflows, anomaly detection, and optimized aggregations. Apply Data Mesh, Data Governance, and Data Lineage principles while supporting data needs across Product and Engineering. Collaborate with analysts, scientists, software engineers, and DevOps teams to build self-service data tools and modern data architecture.

Lead the architecture and hands-on delivery of an Everything-as-Code data mesh, including Kafka and NiFi event streaming, Kubernetes infrastructure, Terraform and GitOps deployment, schema governance, hybrid lakehouse integration, FinOps optimization, resiliency, and distributed observability across multi-cloud and on-premises environments.

Build full-stack data and AI applications, platform services, APIs, GenAI capabilities, and cloud-native infrastructure for Sanofi’s OneMesh ecosystem. Responsibilities include developing with React, TypeScript, Node.js, Python, and FastAPI; implementing RAG and agent orchestration; deploying on AWS and OpenShift; maintaining Terraform infrastructure and CI/CD pipelines; and ensuring security, observability, reliability, and production readiness. The role collaborates with data, machine learning, product, and business teams.

Build and maintain scalable GCP data pipelines, BigQuery data models, enterprise semantic layers, and CI/CD workflows for a modernized marketing data platform. Rebuild legacy pipelines, support DataHub adoption, enforce data quality, optimize performance and costs, and enable secure multi-brand marketing reporting. Collaborate with product, Martech, analytics, and data engineering teams.

Design and operate AI-ready data products, semantic layers, governed datasets, and natural-language analytics experiences on Snowflake or Databricks. Build ETL/ELT pipelines using dbt, Airflow, Snowpark, and PySpark; deploy Cortex or Genie capabilities; develop RAG and conversational analytics applications; and implement governance, security, lineage, auditability, and performance optimization. Partner with Finance stakeholders to define trusted KPIs, hierarchies, business glossaries, and semantic models in a regulated environment.