Top Data Engineer Jobs in Valencia

Designs and operates scalable production data infrastructure for high-volume athlete, sensor, and time-series data. Builds streaming ingestion, graph models, low-latency ML feature systems, evaluation and monitoring infrastructure, and multi-tenant architectures. Ensures data provenance, lineage, observability, reliability, and correctness while collaborating with ML engineers, data scientists, sport scientists, and domain experts. Requires expertise in Python, SQL, streaming technologies, AWS, graph data modeling, causal inference, AI evaluation, and telemetry systems.

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

Build and maintain data infrastructure and pipelines for a globally deployed Customer Data Platform. The role covers CDP development and operations, data quality, incident management, test automation, stakeholder collaboration, requirements negotiation, and platform stability in a DevOps environment.

Build and maintain data infrastructure and pipelines for a globally deployed Customer Data Platform. Manage both development and operations, ensure platform stability, quality, compliance, and test automation, and collaborate with internal teams and implementation partners. Drive issue resolution, negotiate requirements and scope, and introduce technology-enabled process improvements. The role requires SQL, Python, data platform, DevOps, incident management, and Agile delivery experience.

Develop and maintain scalable production data pipelines, machine learning data pipelines, and data flows supporting financial crime detection solutions. Build tools for data science and analytics teams, optimize large-scale datasets, perform data transformation and validation, and collaborate with product, R&D, and customer teams. The role also involves training customers, managing technical relationships, and traveling domestically and internationally when required.

Designs and maintains scalable data pipelines integrating SAP and non-SAP sources. Develops database solutions, data transformations, quality assurance processes, and analytical data models. Optimizes data systems and collaborates with machine learning engineers, data scientists, analysts, and stakeholders. The role requires SAP data and analytics expertise, Python and SQL proficiency, database knowledge, cloud platform familiarity, and strong communication skills.

You will design and develop data solutions, create ETLs using Spark, work on cloud projects, and build scalable data pipelines.

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.

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.

Designs, builds, and maintains scalable data and machine learning infrastructure, pipelines, databases, integrations, self-service tools, and MLOps workflows. The role includes platform expansion, observability, security, data quality, performance optimization, infrastructure as code, CI/CD, documentation, and collaboration with data scientists, analysts, and stakeholders. Strong experience with cloud platforms, Kubernetes, containerization, databases, big data, orchestration, and AI-assisted development is required.

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.

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.

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 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.

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.

Own the company’s end-to-end data platform, including ingestion, S3 data lakes, ClickHouse, dbt transformations, orchestration, and BI/CRM data delivery. Improve reliability, historical accuracy, cost, and metric governance while defining and defending business KPIs. Partner with Customer Success, Sales, Product, and leadership to translate business questions into durable metrics and establish data standards. This is a senior individual contributor role focused on hands-on engineering, analytical rigor, and stakeholder communication.

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.

Leads electrical design for hyperscale and mission-critical data centers across Europe. Responsibilities include managing HV/MV/LV systems, substations, utilities, consultants, permitting, project delivery, construction support, commissioning, and integrated systems testing. The role provides client-side technical leadership, reviews designs and project documents, coordinates multidisciplinary teams, manages risks and milestones, and ensures compliance with standards and operational requirements.

Develop and maintain high-volume data processing pipelines using cloud and distributed-data technologies. Build jobs and libraries supporting data scientists and analysts, while implementing monitoring, alerting, quality checks, testing, and documentation. Contribute to data platform architecture and collaborate with cross-functional teams. The role requires expertise in SQL, data modeling, workflow orchestration, Python, ETL/ELT design, Spark, and AWS.

Develop high-volume distributed data processing jobs using Redshift, Athena, Iceberg, Spark, and Airflow. Collaborate with data scientists, analysts, and product managers to turn methodologies into products, improve system performance, and support data workflows. Mentor junior engineers on architecture and best practices. The role requires strong Python programming, SQL, database, data modeling, and modern lakehouse expertise, with AWS and streaming experience preferred.