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Edgar FeliuEF

Edgar Feliu

Data Architect - DevOps

550 €/día
Barcelona, ES
3-7 años

Tiempo medio de respuesta: 1h

Acerca de Edgar

I’m Edgar, a data engineer and DevOps specialist with 6 years of experience building and operating data integration and orchestration infrastructure.

Foundation skills: Python, SQL, Git, Docker, Kubernetes. Specialized stack: Airflow (Astronomer), Fivetran CDC pipelines, Azure (AKS, Key Vault) and Snowflake, plus PySpark for large-scale batch processing. Familiar with GCP and Hadoop.

Based in Barcelona, working in English and Spanish, available remote across Europe.
  • Inglés

    Bilingüe o nativo

  • Español

    Bilingüe o nativo

  • Francés

    Bilingüe o nativo

  • Catalán

    Bilingüe o nativo

Acepta trabajo presencial
Barcelona (hasta 50 km)

Experiencia

  • NTT DATA
    DevOps - Data Architect
    AGENCIAS DE SUBCONTRATACIÓN
    mayo de 2026 - Hoy (2 meses)
    Barcelona, España
    Data Engineering & Workflow Orchestration

    Orchestrated Enterprise Data Pipelines: Designed, automated, and optimized complex data workflows using Apache Airflow / Astronomer, ensuring highly reliable data delivery for downstream analytics.

    Streamlined Ingestion with Fivetran: Managed and monitored automated Fivetran connectors, troubleshooting ingestion bottlenecks and maintaining high data integrity across diverse source systems.

    DevOps & Cloud Infrastructure (Azure)

    Deployed Astro Remote Architecture: Built and maintained Astronomer Remote infrastructure within Azure using Helm charts, managing scalable AKS (Azure Kubernetes Service) environments.

    Secured & Optimized Cloud Ecosystems: Configured and managed core Azure infrastructure, leveraging Key Vaults for secret management, Blob Storage for scalable data lakes, and Managed Instances for robust database performance.

    Automated CI/CD & Monitoring: Built end-to-end continuous integration and deployment (CI/CD) pipelines to automate infrastructure and DAG deployment.
    Proactive Infrastructure Monitoring: Implemented comprehensive monitoring and alerting frameworks for both Airflow tasks and Azure infrastructure.
    Airflow Kubernetes Docker Python Snowflake
  • Cofidis SA
    Data Architect - DevOps
    mayo de 2024 - enero de 2026 (1 año y 8 meses)
    Barcelona, Spain
    • • Architected end‑to‑end data pipelines to deliver critical KPIs for Risk, Finance, and Fraud, utilizing Data Storytelling to translate complex metrics into a new enterprise semantic layer.
    • • Led a cross‑departmental pilot program to evaluate the strategic migration from SAS Enterprise Guide to Dataiku, aligning technical capabilities with business user requirements.
    • • Directed the migration of legacy operational data infrastructure from AIX servers to a modern Linux environment, ensuring zero downtime and improved system performance.
    • • Designed and automated optimized ETLworkflows for recurringworkloads, leveraging QlikSense for data visualization and proactive monitoring of business‑critical dashboards.
    • • Championed system reliability by monitoring server health (CPU/Disk) and streamlining incident response in strict adherence to Service Level Agreements (SLAs).
    • • Promoted Data Governance and GDPR compliance by implementing robustmetadata management and data lineage trackingwithin the internal analytics ecosystem.
    • • Advocated for the integration of Agentic AI Orchestration, developing Proof of Concepts (PoCs) to demonstrate how autonomous agents could optimize data workflows and decision‑making processes.
    • • Tech Stack: Dataiku, SAS Enterprise Guide, QlikSense, SQL, Python, Linux (AIX), GenAI, Data Governance.
    SAS SQL Dataiku Python Bash
  • GFT Group
    Data Engineer ‑ Big Data
    octubre de 2021 - mayo de 2024 (2 años y 7 meses)
    Barcelona, Spain
    • • Collaborated within a cross‑functional Agile team of five to engineer a financial crime detection engine, identifying irregular behaviors and mitigating institutional risk.
    • • Partnered with business stakeholders to orchestrate the end‑to‑end migration of the Analytics Engine to GCP, leveraging BigQuery and Dataproc to meet evolving data requirements.
    • • Engineered and optimized automated ETL pipelines for scheduled job execution using Bash and Control‑M, significantly improving data pro‑ cessing efficiency.
    • • Automated deployments via CI/CD, Docker, and Terraform (IaC), fostering a culture of technical excellence and environment consistency.
    • • Ensured system stability by providing critical technical support and clear communication to stakeholders during complex production code releases. GCP Dataproc, Composer), Spark (Scala), Airflow, Docker, CI/CD, Terraform, Control‑M, Bash, Hive, Git.
    Apache Spark Hadoop Google cloud Python Scala

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