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Build and optimize Azure-based data pipelines and architectures, implementing scalable cloud solutions with Azure Data Factory, Databricks, and PySpark.
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.
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.
Build and maintain scalable data pipelines and lakehouses using Microsoft Fabric and Power BI, automating ingestion, transformation, and CI/CD workflows for analytics solutions.
Build and maintain AWS-based data pipelines and ETL workflows using Python, Spark, and Glue to feed analytics and reporting systems.
Designs and maintains data pipelines using Microsoft Fabric and Azure tools to build scalable analytics platforms for enterprise clients.
Build end-to-end data pipelines and analytics solutions for Oysho’s eCommerce platform using Databricks, PySpark, SQL, and Power BI to drive business decisions.
Builds and scales industrial data pipelines in Azure/Databricks, integrating sensor data with ML models to improve energy efficiency and logistics in a European industrial company.
Design and build cloud-native data pipelines on GCP to power analytics and AI, ensuring scalable, governed, and secure data platforms for enterprise use.
Builds and maintains data pipelines on Azure and Microsoft Fabric, ingesting, processing, and modeling data with PySpark, ADF, and KQL for analytics and reporting.
Designs and builds scalable data pipelines on GCP for a capital-markets fintech, integrating financial data sources and optimizing warehouse/lake solutions.
Designs and builds end-to-end cloud data platforms using Microsoft Fabric and Azure, setting standards and guiding teams to create scalable, reusable data architectures and pipelines.
Build and maintain the automation, monitoring, and cloud infrastructure for a next-gen data lakehouse platform serving a financial-sector product.
Build and maintain AWS-based ETL pipelines for a banking client, migrating legacy systems to a Lakehouse architecture using PySpark and Python.
Lead a team of trainees to build scalable GCP-based data pipelines using BigQuery, Airflow, and Dataflow, while mentoring junior engineers and enforcing data governance standards.
Build and maintain modern data platforms (lakehouse, data warehouse) and ETL/ELT pipelines in cloud and on-prem, integrating structured and unstructured sources to enable analytics and AI solutions.
Build and maintain a modern Azure Databricks lakehouse platform, designing robust data pipelines and models to power analytics and AI-driven insights for a large retail chain.
Build and maintain scalable data pipelines and products for Tryg’s AI-driven insurance pricing, portfolio management, and underwriting using Databricks, SQL, and Python.
Build and maintain AWS-based data pipelines for a bank using Databricks and PySpark on EMR/Glue.
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