Top Data Engineer Jobs in Valencia

Build and operate production data pipelines supporting analytics, AI, and agentic workflows. Responsibilities include implementing canonical data models, maintaining Databricks or Snowflake platforms, monitoring reliability, responding to incidents, validating healthcare data mappings, improving performance and cost, and documenting architecture. Requires strong SQL and Python skills, cloud data platform experience, ETL/ELT orchestration expertise, and healthcare or pharmaceutical data experience.

Build and manage Python-based data ingestion workflows and pipelines, integrate data with ServiceNow and downstream systems, optimize Snowflake storage and OpenTelemetry observability, troubleshoot pipeline failures, and implement validation checks to maintain data integrity.

Designs, builds, and operates production-grade agentic AI systems and orchestration frameworks. Responsibilities include prompt architecture, tool and API integrations, monitoring, evaluation, cost controls, reliability improvements, and governance documentation. The engineer collaborates with data science, data engineering, and governance teams to ensure reliable, compliant workflows using structured healthcare and pharmaceutical data.

Build unstructured-data ingestion and context-layer pipelines for an investment group. Responsibilities include historical backfills, document parsing, entity resolution, deduplication, incremental API synchronization, embeddings, vector and graph storage, access scoping, lineage, PII protection, orchestration, monitoring, cost control, and operational runbooks.

Own the data engineering side of DoiT’s integrations framework by building and maintaining third-party billing and usage API integrations. Ensure data correctness, completeness, normalization, reconciliation, and support for negotiated rates. Manage orchestration, scheduling, backfills, and pipeline operations while using AI throughout development. Collaborate with product, support, and engineering teams to expand vendor coverage and improve cloud and SaaS spend visibility.

Own data engineering for a cloud and SaaS billing integrations platform. Build and maintain third-party billing API integrations, operate ingestion pipelines, normalize vendor data, manage orchestration and backfills, and ensure financial-grade correctness through reconciliation and quality checks. Use AI throughout the engineering workflow and collaborate with product, support, and platform teams to expand vendor coverage and improve customer cost visibility.

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.

Develop and support modern data pipelines and Lakehouse architectures using Azure Databricks, Spark, PySpark, SQL, and Azure Data Factory. Integrate multiple data sources with SQL Server data warehouses, maintain analytical data models, optimize large-scale processing, ensure data quality and performance, and collaborate with BI teams supporting Power BI and reporting platforms. Participate in cloud data architecture implementations under senior guidance.

Own and scale secure cloud infrastructure for data and AI platforms across AWS and GCP. Lead Terraform-based infrastructure as code, reliability and observability programs, GitLab CI/CD automation, Docker and Fargate deployments, and platform enablement for Airflow, dbt, streaming, and AI workloads. Establish security, access-control, cost-governance, monitoring, alerting, and SLO practices while partnering with data engineering teams.

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.

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 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.

Build and own end-to-end data foundations, including ingestion pipelines, dbt models, data marts, semantic layers, and AI-ready datasets. Integrate fragmented marketing, advertising, ecommerce, analytics, and CRM data across a multi-tenant Snowflake environment. Manage platform API changes, data quality, restatements, performance, and client-specific modeling. Collaborate with product, engineering, AI, BI, and client teams while using AI coding agents to accelerate reliable production development.

Build scalable data architectures and pipelines for API, batch, event, and streaming sources. Develop aggregation, preprocessing, governance, security, and lifecycle processes; support cloud OLTP and OLAP workloads; and integrate machine learning and analytics components, including MLOps. Collaborate with data science and application development teams to create, deploy, monitor, and maintain secure data solutions using AWS, containerization, infrastructure as code, and CI/CD.

Build and own the architecture for Dune’s curated data lifecycle, including dependency-aware orchestration, backfills, schema evolution, retries, recovery, data quality, and alerting. The role combines backend software engineering with large-scale data systems work across Go, Kotlin, Rust, Python, and SQL. Responsibilities include designing ingestion and curation contracts, making architecture decisions, solving ambiguous problems, and leading work that other engineers can execute.

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.

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.

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

Freelance Senior Data Solution Engineer responsible for delivering approximately two custom client data projects monthly. Duties include qualifying requirements, analyzing large datasets with Python and SQL, building dashboards and predictive models, developing business recommendations, presenting findings to clients, documenting projects, and sharing insights with Product. The role requires fluent Spanish and English, freelance status, and availability for a full-time four-to-five-month mission based in Barcelona or Madrid.

Design and build durable, distributed data-streaming infrastructure that decouples process data from Camunda’s live execution engine. Develop customer-facing integrations such as Kafka connectivity, support analytics data consumption at scale, and make architecture decisions around consistency, durability, and storage. Participate in production support and on-call rotations while collaborating with Product, Data Layer, Distributed Systems, QA, SRE, and Support teams. Lead ambiguous, cross-functional engineering initiatives from design through delivery.

Build and maintain Camunda 8 backend data-layer services spanning relational databases and Elasticsearch/OpenSearch. Design scalable storage, query, retention, and streaming systems; improve performance, indexing, and production tooling. Lead engineering initiatives from solution design through delivery, collaborate across teams, provide L3 support, and participate in on-call rotations. Investigate production data issues and contribute to schema evolution, workflow automation, and CI improvements.

Design, implement, and test cloud-based server-side software in Python. Optimize code performance, maintain unit and integration tests, and work with AWS, Docker, DevOps, and CI/CD tools. Preferred experience includes reverse engineering public APIs, PostgreSQL, Django ORM and DRF, network protocols, and web or mobile technologies.

Own the design, delivery, and operation of complex data pipelines, datasets, and platform components. Translate business needs into scalable data models and reliable data products, improve data quality and observability, resolve production issues, and establish reusable engineering tools and standards. Collaborate with product and business stakeholders, contribute to architecture decisions, mentor engineers, and participate in on-call support.