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Build and scale cloud-based data pipelines and platforms using AWS, Python, PySpark, and Databricks to deliver reliable, high-performance data solutions for analytics and business needs.
Build and optimize cloud data pipelines for analytics using Azure Data Factory, Databricks, and Power BI to enable strategic decision-making for enterprise clients.
Designs and builds data pipelines using Microsoft Azure Synapse, Databricks, and SQL Server to process and model data for analytics and reporting.
Design and implement Azure-based streaming-to-batch data pipelines using Data Factory, Data Lake Storage, and PySpark/Scala in Python.
Build and maintain data pipelines and distributed systems for European clients, using Python, Spark, and cloud platforms to solve business challenges in industries like pharma, insurance, and manufacturing.
Lead the design and implementation of a scalable data platform (Salesforce Data 360) and data lake, integrating ERP, CRM, and BI tools while ensuring governance and compliance.
Build and maintain an insurance-focused data warehouse, automate ETL pipelines, and create Power BI dashboards to track claims, conversion, and retention KPIs.
Build and maintain scalable data lake components and ingestion pipelines for analytics, ML, and product features at an online education platform.
Build and maintain data pipelines in Python and Airflow to ensure BigQuery data remains accurate and traceable for a European scale-up.
Build and maintain large-scale data pipelines and cloud infrastructure using AWS, Spark/Scala, Python, and SQL to power analytics and ML workloads.
Build and maintain scalable data pipelines using Microsoft Fabric to integrate and optimize data from ERP, CRM, and APIs for analytics and reporting.
Designs and maintains cloud-based data pipelines and warehouses using Python, PySpark, SQL, and Databricks to power analytics and ML on Microsoft Azure.
Build and maintain cloud-based data pipelines for a global fintech firm, using Python, Spark, and Azure to enable analytics and reporting.
Build and optimize Azure-based data pipelines and architectures, implementing scalable cloud solutions with Azure Data Factory, Databricks, and PySpark.
Build and maintain cloud-based data pipelines using Azure services (Fabric, Synapse, Data Lake) and Python/SQL to turn raw data into insights for clients.
Build and maintain scalable cloud data pipelines for a global travel platform, using AWS, Kafka, Spark, and Scala to process billions of daily events.
Build and optimize Azure-based data pipelines and cloud architectures, using Azure Data Factory, Synapse, PySpark, and Medallion/Lakehouse patterns to move and transform data securely and at scale.
Designs and builds scalable data pipelines and cloud architectures on Google Cloud Platform, ensuring high-quality data for analytics and AI workloads.
Designs and maintains AI-augmented data extraction pipelines using Pydantic, Scrapy, and LLM agents to autonomously scrape and validate structured data from websites while ensuring compliance and reusability.
Design, build, and optimize Azure-based data pipelines and cloud architectures, using PySpark, Azure Data Factory, Synapse, and Lakehouse patterns to move and transform data securely and at scale.
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