Top Data Engineer Jobs in Valencia

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.

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.

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 evolve a Microsoft Fabric-based global data platform: create scalable lakehouse pipelines, medallion architecture, analytical models, reusable engineering patterns, and governance. Embed security, GDPR/PII controls, CI/CD, IaC, and mentor domain teams to modernize legacy BI/DW solutions.

Design, build, and operate a scalable cloud-based data platform using IaC, CI/CD, and container orchestration. Implement and maintain data pipelines, observability, storage/processing layers, and platform best practices for reliability, security, and cost efficiency while collaborating with cross-functional teams.

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.

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.

Build and maintain high-performance market data infrastructure in Rust to ingest, process, store, and serve real-time pricing feeds. Solve latency, data quality, and cross-exchange consistency problems, contribute trading/market-data expertise to engineering decisions, communicate with a distributed team, and potentially lead a small team over time.

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.

Own the finance data domain by developing financial data products and models, integrating and improving data quality, monitoring freshness and completeness, and resolving reconciliation issues. Build reports, alerts, automations, and LLM agents using Python, SQL, Snowflake, Airflow, and dbt. Communicate data issues clearly to Finance leadership and ensure financial reporting and planning data remains accurate and reliable.

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

The Staff Engineer will define architecture, lead data pipeline design, ensure platform reliability, and mentor engineers, driving data initiatives and technical standards.

Design and maintain data warehouse systems, develop ETL pipelines, manage datasets for analytics, and implement real-time data solutions.

As a Data Analytics Engineer, you'll design and maintain ETL pipelines, ensure data quality, and collaborate with various teams to meet data needs.

The role involves creating and automating infrastructure features for data platforms using Python, with a focus on distributed systems and collaboration within a global team.

The role involves automating OpenSearch operations by developing features in Python, collaborating with an international team, and providing expertise on data platforms.

As a Software Engineer on the data platform team, you'll automate data platform operations, write Python code, and collaborate on solutions within a distributed team.

Design, build, and own scalable data platforms across Azure, AWS, and Snowflake. Architect warehouses, semantic layers, ELT/ETL pipelines, and AI/ML data infrastructure (feature stores, vector DBs, RAG/LLM-ready). Lead standards for modeling, observability, CI/CD, automation, governance, and mentor teams while partnering cross-functionally to drive data strategy.

Lead design and ownership of a cloud-native data platform: build scalable batch ETL and ingestion pipelines, develop analytical models (Athena/Redshift/ClickHouse), integrate data workflows with microservices on ECS, ensure data quality/observability, performance tuning, implement IaC and CI/CD, and mentor engineers.

Maintain and expand a high-scale data platform: build and run data pipelines, implement DataOps, collaborate with analytics teams, ensure data quality and security, and design enterprise data models.

Join Zencore as a Senior Data Engineer, leveraging Google Cloud expertise and technology strategies to assist clients with data and application modernization.

Designs and maintains scalable, real-time data infrastructure, including ingestion, pipelines, storage, analytics, data models, APIs, and observability. Develops AI-powered workflows and integrates AI services into cloud-native SaaS products. Owns features through production, ensures data quality and reliability, collaborates with cross-functional engineering and product teams, and supports production-scale systems using streaming, cloud, containerization, and relational database technologies.

Design and build scalable real-time and batch data pipelines for programmatic advertising, integrating SSP, DSP, ad server, and reporting data. Develop data models, warehouses, lakes, ETL/ELT workflows, auction analytics, yield optimization, and quality monitoring. Support BI teams with performant datasets while ensuring GDPR and CCPA compliance. The role requires ownership of architecture through deployment and collaboration with engineering and analytics teams.

Design and scale mission-critical market data services for stocks, options, crypto, and foreign exchange. Responsibilities include architecting backend systems and APIs, maintaining software quality and test coverage, participating in code reviews, and troubleshooting incidents and bugs. The role uses Go and Python with REST, gRPC, WebSockets, SSE, Docker, Kubernetes, Google Cloud, BigTable, Prometheus, Grafana, and ELK.