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Build and maintain cloud-based data infrastructure for AI-driven systems, designing scalable pipelines with tools like Airflow, Spark, and cloud platforms to securely store and process large datasets.
Build and optimize data pipelines and warehouses for clients using Azure Data Factory, Databricks, Python, and CI/CD to enable data-driven decisions in sectors like energy, healthcare, and logistics.
Build and maintain data pipelines, data warehouses, and production-ready ML systems for clients like ING or ASML using Python, Spark, Kafka, and cloud tools.
Builds and maintains ITSM data pipelines on Azure and Databricks, implementing business rules and ETL workflows for a global data platform used by Rabobank.
Builds and maintains cloud-based data pipelines and infrastructure for clients, using tools like Databricks, Spark, Airflow, and Terraform to process and store data at scale.
Build and maintain a scalable data platform for a quantitative investment firm, using Python, Airflow, and AWS to process market data and support research workflows.
Build and engineer Microsoft Fabric lakehouses and data pipelines for enterprises, defining best practices, CI/CD, and scalable architectures while bringing software engineering rigor to data platforms.
Lead a team to build and scale a modern data platform using ELT/ETL pipelines, dbt, Airflow, and cloud warehouses like Snowflake or Databricks to drive reliable, high-performance analytics for business decisions.
Build and maintain scalable data pipelines and datasets to power customer messaging, rewards, and analytics for Booking.com’s travel platform.
Design and build SQL Server-based data pipelines and enterprise data warehouse for a European bank, modernizing legacy systems with Python, Airflow, and Azure while ensuring regulatory compliance.
Senior Data Engineer builds and maintains scalable cloud data pipelines and models for clients in mobility, energy, and finance, using Python, SQL, and cloud stacks like Azure/AWS/GCP.
Build scalable data pipelines and realtime analytics platforms in Python, Spark, and cloud (GCP/AWS/Azure) for enterprise clients like KLM and ASML.
Senior Data Engineer builds scalable, secure data platforms for banking, logistics and manufacturing clients using Databricks, PySpark, SQL and cloud orchestrators.
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 maintain data pipelines and warehouses for cutting-edge clients using Python, SQL, and cloud tools like Azure Synapse and Databricks.
Lead a data engineering team to build and maintain Python-based ELT pipelines into BigQuery, design SQLMesh models, and set engineering standards for a modern data stack.
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.
Design and maintain scalable data pipelines and storage systems to power fraud detection, analytics, and reporting using Python, SQL, and tools like Airflow and ClickHouse.
Build and deploy cloud-native data platforms on Kubernetes, containerizing workloads and integrating tools like Airflow and Spark while collaborating with data scientists to productionize models.
Build and maintain scalable data pipelines and platforms for enterprise clients using cloud tools (AWS/Azure/GCP), SQL, Python, and orchestration frameworks like Airflow.
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