Top Data Engineer Jobs in Seville

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.

Own and evolve a GCP-native data platform from ingestion to reporting. Lead architecture, reliability, observability, data quality, cost optimization, and AI-assisted engineering practices while mentoring engineers and collaborating with product and customer teams.

Design and implement end-to-end data architecture (Postgres CDC -> Kafka -> Snowflake). Own ingestion, orchestration, transformation, streaming pipelines, and data quality. Define data contracts, tenant separation, replay/reload, and external delivery. Start hands-on and grow a small data engineering team with a 3/6/12-month roadmap.

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.

Own and scale AWS OpenSearch production infrastructure and PostgreSQL-to-OpenSearch data pipelines. Design indices and mappings, optimize queries and bulk indexing, monitor cluster health, implement ingestion reliability, perform reindexing/backfills, build reconciliation and recovery processes, and collaborate with backend, DB, and product teams on searchable schemas and runbooks.

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.

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.

Own and scale AWS OpenSearch infrastructure and PostgreSQL-to-OpenSearch pipelines. Design indices, mappings, analyzers, and retention policies. Optimize queries and bulk indexing, monitor cluster health, implement retries/idempotency, backfills, reconciliation, and disaster recovery. Build dashboards, alerts, runbooks, and participate in incident response while collaborating with backend, DB, infra, and product teams.

Design, build, and maintain scalable ETL/ELT pipelines and cloud data warehouse transformations. Create dimensional schemas and semantic models, enforce automated data validation, optimize query performance and partitioning, manage backend data layers for BI, and collaborate with business and engineering teams to translate requirements into robust analytics architectures.

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.

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.

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.

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.

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

Own, run and evolve the Data Privacy Orchestrator that manages data retention, deletion, anonymization and pseudonymization. Build safe-by-default platform abstractions, self-serve tooling, docs and observability; operate production (deploys, alerting, on-call), align legal/security/product, and extend platform coverage across data stores and third parties as the company expands.

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 maintain Snowflake-based data assets and metrics layers using dbt, SQL, and Python. Design reliable data models and pipelines, enable self-service analytics, collaborate with stakeholders, document definitions and best practices, and deliver scalable analytics solutions.