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Lead a Paris-based data engineering squad, overseeing pipeline development and coordinating with IT and Data product teams using Python, PySpark, and SQL.
Middle AI Engineer at MTS Web Services (Finance Block) building and maintaining ML/DL/LLM infrastructure, fine-tuning models, developing RAG search and document recognition systems, and creating production data pipelines using Python, PySpark, Airflow, and vector databases.
Full Stack Developer builds and optimizes backend services and middleware for a government-focused project in Chantilly, VA, using Java, Solr/Elasticsearch, and big data tools.
Builds and maintains the AI and data platform that powers Afresh's grocery-focused products, focusing on retrieval, agent systems, and evaluation infrastructure for LLMs.
Designs and scales data platforms for analytics, product insights, and ML, leading architecture discussions, optimizing pipelines, and mentoring teams to improve data reliability and quality for a fintech company.
Senior engineer builds and operates the AI platform that turns raw grocery data into reliable context for LLMs and agent systems, including retrieval layers, evaluation harnesses, and production serving infrastructure.
Design, build, and maintain data pipelines, data models, and analytics-ready datasets using Microsoft Fabric, Azure data services, and medallion architecture patterns for a government education institute.
Data Engineer building and maintaining data pipelines and analytics-ready datasets using Microsoft Fabric, Azure data services, SQL, and Python within a government education institution.
Design, develop, and optimize scalable ETL/ELT data pipelines using Python, PySpark, and SQL for a Singapore-based consulting firm serving banking clients.
Build and maintain scalable data pipelines on Databricks using PySpark and Python to deliver government analytics solutions.
Data Engineer designing, developing, and optimizing scalable ETL pipelines and data warehouses using Python, PySpark, SQL, and cloud data platforms for large-scale datasets in Singapore.
Data Engineer responsible for designing, developing, and maintaining scalable data pipelines on Databricks and Azure, supporting analytics and ML workloads with ETL, streaming, and batch processing.
Oversees data ingestion pipelines for analytics and AI, designing and maintaining frameworks to extract, validate, and integrate data from multiple sources into a data lake, ensuring performance, automation, and SLA compliance.
Data Scientist/ML Engineer responsible for the full data lifecycle—designing, training, and deploying machine learning models in production environments using Python, SQL, and ML frameworks.
Technical Data Steward managing data quality, profiling, and SQL queries using Azure Databricks notebooks, based on-site in Barcelona or Madrid.
Data Engineer in Madrid designing and optimizing data pipelines with PySpark and SQL on AWS, including containerized deployments for scalable analytics workloads.
Senior Data Engineer designing and evolving ETL/ELT processes, implementing data ingestion/transformations in Data Lake/Warehouse/Lakehouse, ensuring data quality, and acting as technical lead. Core technologies: Databricks, Python, SQL, Spark.
Data Engineer / ETL focused on Microsoft Fabric, building ETL/ELT processes, Lakehouse architectures (Bronze/Silver/Gold), data modeling, and integration using Python, PySpark, SQL, and Power BI.
Builds and maintains scalable data pipelines for EA’s Localization team, processing structured/unstructured data to support AI/ML and analytics. Focuses on ETL/ELT, cloud-based solutions, and ensuring data integrity for global game localization.
Data Engineer at Accenture España designing and implementing modern, scalable data architectures (ETL, data ingestion, data quality) for top clients using Python, PySpark, SQL/NoSQL, Big Data, and cloud technologies in a hybrid role based in Barcelona or Madrid.
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