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Build and maintain cloud-based data pipelines and Lakehouse architectures using Azure, AWS, Databricks, and PySpark to deliver clean, scalable data for analytics and AI teams.
Senior Data Engineer builds and maintains real-time, cloud-native data pipelines handling billions of daily events, modernizing legacy systems into scalable lakehouse architectures.
Designs and builds cloud data pipelines on Azure Databricks, integrating PySpark, Delta Lake, and Azure Data Factory to power analytics and BI for enterprise clients.
Build and maintain scalable data pipelines and lakehouse platforms using Databricks, Spark, Delta Lake and Azure to support anti-financial-crime analytics and reporting for PwC’s global clients.
Build and maintain scalable data pipelines and Lakehouse models on Databricks, using PySpark, Delta Lake, and Medallion architecture to deliver analytics-ready datasets for enterprise clients.
Build and maintain scalable Azure data pipelines and lakehouse solutions using Databricks, PySpark, and Azure Data Factory to process and transform large datasets for enterprise clients.
Build and maintain scalable Azure data pipelines and Databricks lakehouse solutions, transforming raw data into analytics-ready assets while collaborating with cross-functional teams.
Build and maintain scalable data pipelines and cloud data platforms for banking clients, using Spark, Scala, and cloud services to deliver clean, governed data for analytics and AI.
Build and maintain scalable data pipelines and lakehouse architectures on Databricks, using SQL, Python, and dbt to deliver clean, governed data for clients in finance, government, and healthcare.
Build and maintain scalable data pipelines and lakehouse platforms using Databricks, Spark, Delta Lake, and Python to support anti-financial-crime analytics and reporting for global PwC clients.
Build and maintain scalable data pipelines and Lakehouse architectures using Databricks, PySpark, and Delta Lake to deliver analytics-ready datasets for GenAI and business use cases.
Lead a team to build and optimize Databricks-based ETL/ELT pipelines using Python/SQL, Delta Lake, and cloud platforms (Azure/AWS/GCP) for large-scale data processing.
Lead a Databricks-based data engineering team to build and optimize ETL/ELT pipelines, Delta Lake tables, and cloud data warehouses using Python, SQL, and Azure/AWS/GCP.
Build and scale Straumann’s AI data infrastructure, focusing on a Data Lakehouse, dataset versioning (DVC), and reliable pipelines that power dental AI products.
Build and maintain Mettler Toledo’s data platform, creating scalable pipelines and ensuring high-quality data flows for analytics and AI/ML using Databricks, Snowflake, and Python.
Design and maintain high-scale data pipelines processing billions of ad events daily, modernize legacy systems into cloud-native lakehouse architectures, and ensure low-latency, privacy-compliant data delivery for monetization and decisioning.
Build and maintain scalable data pipelines and modern data platforms (Snowflake, cloud) to support analytics and AI use cases for international clients.
Builds and optimizes scalable ETL pipelines using Spark, Delta Lake, and Azure Databricks to feed AI/ML models and analytics for a global gambling platform.
Designs and maintains high-scale data pipelines processing billions of ad events daily, modernizes legacy ad-tech systems into cloud-native lakehouse architectures, and ensures reliable, low-latency data delivery for monetization and analytics.
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