Daniel Gesicki

Daniel Gesicki

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Senior Data Engineer at Feedzai
Madrid, Comunidad de Madrid, Spain
Contact Daniel regarding: 
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Full-time jobs
Starting at EUR60k/year ~USD69.6k/year

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Résumé

Jobs verified_user 0% verified
  • Feedzai
    Data Engineer
    Feedzai
    Mar 2024 - Sep 2025 (1 year 7 months)
    Architected and optimized **Databricks-based data lakehouse pipelines**, processing 100M+ transactions daily and improving fraud-detection analytics latency by 38%. Implemented **ETL automation using Airflow and Python**, reducing manual data operations time by 45% across multi-cloud environments. Integrated **MicroStrategy dashboards** with Spark and SQL data models, enabling real time insights for compliance and fraud teams. Enhanced **data governance and observability** using metadata-driven ingestion and version-controlled models with dbt. Developed RESTful APIs for data consumption layers, reducing report generation time from hours to minutes. Optimized Spark jobs and Databricks notebooks, cutting compute costs by 20% through dynamic r
  • KPMG
    Data Engineer
    KPMG
    Feb 2022 - Feb 2024 (2 years 1 month)
    Designed **ETL frameworks in Databricks** to consolidate multi-source financial data, cutting data processing times by 50%. Built **interactive MicroStrategy dashboards** for risk and audit analytics, improving visibility for 40+ enterprise clients. Collaborated with cloud teams to deploy **data warehouse solutions on Azure and AWS**, enabling hybrid analytics scalability. Developed automated QA pipelines using Pytest and SQL test frameworks, improving data reliability and integrity by 99%. Integrated **Airflow DAGs** for compliance reporting workflows, enabling timely submissions for regulatory filings. Engineered **Python-based ELT scripts** with modularity for reusability across client engagements. Enabled cost optimization by configurin
  • Addepto
    Junior Data Engineer
    Addepto
    Jun 2017 - Jan 2022 (4 years 8 months)
    Developed foundational **ETL pipelines using Python and SQL**, improving data ingestion throughput by 200% for analytics clients. Assisted in **MicroStrategy dashboard creation** and report automation, reducing manual reporting time by 60%. Contributed to **data model standardization** projects for tourism and logistics clients, improving scalability and maintainability. Implemented **data cleansing routines** that increased dataset accuracy and usability for downstream analytics models. Collaborated on **migration from on-prem to AWS cloud**, optimizing data transfer pipelines via S3 and Redshift. Built and maintained **REST APIs** for internal data catalog and metadata management. Supported senior engineers in debugging Spark jobs and opt
Education verified_user 0% verified
  • Military University Of Technology
    Bachelor's Degree in Information Technology
    Military University Of Technology
    May 2024 - Jun 2025 (1 year 2 months)
  • E
    Excellence in Data Engineering
    Mar 2023 - Jul 2023 (5 months)
    Recognized for successful delivery of cost-saving data solutions and impactful BI modernization projects.
Projects (professional or personal) verified_user 0% verified
  • Feedzai
    Databricks-Based Data Lakehouse Pipelines
    Feedzai
    Mar 2024 - Sep 2025 (1 year 7 months)
    Architected and optimized Databricks-based data lake house pipelines processing 100M+ transactions daily, improving fraud-detection analytics latency by 38%.
  • KPMG
    ETL Frameworks for Financial Data Consolidation
    KPMG
    Feb 2022 - Feb 2024 (2 years 1 month)
    Designed ETL frameworks in Databricks to consolidate multi-source financial data, cutting processing times by 50%.
Awards verified_user 0% verified
  • E
    Employee of the Quarter
    May 2024 - May 2025 (1 year 1 month)
    Awarded for outstanding work optimizing fraud-detection pipelines and delivering high-availability data solutions.
Publications verified_user 0% verified
  • E
    ETL Automation in Multi-Cloud Environments
    Jan 2023 - Jan 2024 (1 year 1 month)
    Authored an article discussing ETL automation and best practices for multi-cloud environments (AWS, GCP, Azure).