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Senior Data Engineer designs, implements, and optimizes industrial data historian systems (e.g., AVEVA PI) and integrates them with control systems and third-party apps.
Build and maintain AWS-based data pipelines using Python, PySpark, and SQL to integrate, transform, and deliver clean, scalable data for business insights.
Build and evolve a global data platform using Microsoft Fabric, lakehouse architecture, and Azure, designing scalable pipelines and reusable data models for analytics and AI.
Build and optimize data pipelines for a fleet-management platform, processing vehicle telemetry with AWS and Databricks to power analytics and ML models.
Design and maintain ETL/ELT pipelines and API integrations to unify data from multiple sources into a reliable foundation for analytics and business teams.
Senior PySpark Data Engineer builds and optimizes data pipelines in cloud environments (AWS/Azure) using PySpark, Hadoop, and SQL Server for clients across industries.
Senior Data Engineer role at Sopra Steria in Madrid, Spain, designing and building data pipelines and architectures using PySpark.
Build and optimize AWS-based data pipelines and warehouses using ETL, cloud architecture, and database systems to support analytics and decision-making.
Build and optimize AI-powered data pipelines for pharmaceutical sales using Snowflake, AWS, and dbt to transform and scale data workflows in a global engineering team.
Build autonomous AI agents and RAG systems to automate marketing workflows, integrating LLMs, vector databases, and cloud platforms like GCP/AWS.
Build and maintain cloud-based data pipelines and ETL processes to deliver clean, accessible datasets for enterprise clients in advertising and marketing technology.
Designs and maintains cloud data pipelines and analytics platforms for food/beverage plants using Azure, Databricks, and time-series databases to enable AI-driven insights and operational reporting.
Design and automate AWS-based data pipelines to ingest logistics data, build trusted datasets, and support business decisions across Amazon’s network.
Design and build scalable data pipelines and warehouses, then integrate them with ML and GenAI systems to power analytics and business solutions.
Build and maintain a cloud-based data lakehouse, design scalable data models, and develop ETL/ELT pipelines in AWS for analytics use cases across marketing, HR, and operations.
Design and maintain scalable data pipelines and warehouses for a global ad-tech platform, ensuring real-time data availability and quality for analytics and decision-making.
Design and maintain scalable data pipelines and ETL/ELT workflows using Databricks, Azure Data Factory, and PySpark for cloud-based data platforms.
Builds Azure-based data pipelines, lakehouse architectures, and Power BI reports using Data Factory, Databricks, and SQL.
Build and maintain data pipelines using Spark, PySpark, and AWS services to process batch and real-time data for clients.
Designs, builds, and maintains data integration pipelines and Big Data solutions for a banking client, ensuring scalability and reliability.
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