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.

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

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.

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.

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 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.

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.

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.

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

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

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

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 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.

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

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.

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

Build a greenfield data platform and ingestion pipelines for payments, marketing, and operational data across AWS and GCP; implement a Lakehouse with medallion-layer transformations in dbt/SQLMesh (Redshift/BigQuery); own orchestration (Dagster/Airflow), lineage, backfills, retries, alerting; apply IaC with Terraform and cost controls; operate autonomously in a remote, async environment.

Build and own end-to-end features for Fever's native Android app: shape product with PM/Design, implement Kotlin/Compose solutions in a multi-module Gradle codebase, integrate APIs and payment SDKs, instrument experimentation/analytics, iterate from data, and bridge AI/data capabilities into the user experience.

Own and operate Databricks-based data platform production support, build and migrate ETL/ELT pipelines to Databricks across Azure/AWS, optimize compute and performance, enforce Delta Lake architecture and data security, implement observability and CI/CD, support multi-tenant onboarding and on-call incident response, and maintain runbooks and SLA metrics.

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.