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Build and maintain GCP-based data pipelines and data lakes for a finance client, using Scala and Spark to industrialize ML/OR models and replace legacy warehousing systems.
Build and maintain scalable data pipelines and infrastructure using Python, Scala, Kafka, Airflow, and Kubernetes to power analytics and ML at a media-tech company.
Builds scalable data pipelines and analytics using Microsoft Azure, Fabric, and Quantexa for financial and regulatory clients.
Build and maintain scalable data architectures using Azure, Fabric, and Quantexa, developing pipelines, APIs, and analytics for financial and regulatory use cases.
Design and operate Microsoft Fabric-based data platforms, building scalable ETL/ELT pipelines in Python and PySpark to support clean-energy infrastructure analytics.
Build and maintain Azure-based data pipelines and ETL workflows using SQL, Python, and Azure Data Factory to support analytics and reporting for banking/finance clients.
Designs and builds scalable Azure data pipelines using Databricks and Data Lake Storage, leveraging Python/Scala to enable analytics and cloud-native data solutions.
Build and advise on scalable data platforms and pipelines for Dutch enterprises using Python, SQL, and cloud tools like AWS/GCP/Azure.
Design and build large-scale data pipelines in Python/Scala and Spark to curate high-quality datasets for training next-gen AI models at TB/PB scale.
Design and build cloud data pipelines on Databricks, using Python or Scala to integrate and process data for clients.
Build and maintain data pipelines and models in Azure Databricks and Synapse to deliver analytics and ML-ready datasets for a fresh-produce company.
Build and maintain data pipelines, integrate sources, and enable analytics for clients using SQL, Python, and cloud platforms like AWS/Azure.
Build and maintain scalable data pipelines and datasets to power customer messaging, rewards, and analytics for Booking.com’s travel platform.
Build scalable data pipelines and realtime analytics platforms in Python, Spark, and cloud (GCP/AWS/Azure) for enterprise clients like KLM and ASML.
Build and optimize Azure-based data pipelines using Python, Spark-SQL, and Azure services like Databricks and Data Factory.
Build and maintain scalable data pipelines using Python, PySpark, and SQL, applying software engineering best practices to data architectures in Azure and GCP.
Build and optimize data pipelines and architectures for clients, using Python, SQL, and cloud platforms like AWS/Azure to process and integrate data at scale.
Principal Data Engineer designs and builds scalable, sovereign data platforms for government and enterprise clients, integrating cloud-native pipelines, AI, and open-source tech like Kafka, Spark, and Kubernetes.
Leads end-to-end data engineering projects, designing scalable ETL/ELT pipelines and cloud-based data architectures while coaching a team of engineers to deliver reliable, high-impact data solutions for enterprise clients.
Build and maintain a self-serve data platform for a large classifieds marketplace, owning batch and streaming pipelines, lake management, and APIs that power analytics and ML workloads using Databricks, AWS, Spark, Python, Kafka, and Airflow.
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