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Senior Data Engineer designs and optimizes Azure Synapse data pipelines, builds star schemas, and tunes SQL pools for analytics workloads using Azure Data Factory and Databricks.
Build and maintain scalable data pipelines and analytics datasets using Azure Databricks, Microsoft Fabric, and Python, enabling reporting and AI use cases for enterprise clients.
Designs and builds data pipelines, ETL processes, and streaming ingestion using Spark, Kafka, and cloud tools to feed analytics platforms.
Designs and maintains scalable data pipelines and cloud-based data solutions to support analytics, reporting, and machine learning initiatives using Python, SQL, and cloud platforms like AWS/Azure/GCP.
Build and maintain scalable data pipelines on Databricks and AWS, focusing on ETL, real-time streaming with Spark/Kafka, and cloud infrastructure optimization.
Build and migrate ETL/ELT pipelines on Databricks and AWS for a global fund-services provider, using Delta Lake, Spark, and AWS Glue.
Lead a team to build and optimize scalable data pipelines using Snowflake, Databricks, and Kafka, ensuring security, governance, and performance for a global fast-food company.
Designs and optimizes Azure Synapse data pipelines, builds star schemas, and monitors performance using Dynamic Management Views for analytics workloads.
Designs and builds data pipelines to ingest, process, and store structured and unstructured data using cloud and on-prem tools like Spark, Kafka, and HDFS.
Designs and maintains scalable data pipelines and warehouses (e.g., Fabric, Databricks) to collect, store, and analyze data for analytics and ML, using SQL, Python, and cloud platforms.
Build and maintain scalable ETL pipelines and real-time data systems on AWS and Databricks for a regulated crypto exchange serving 18M+ users.
Build and scale data pipelines using Spark, Python/Scala, and cloud platforms (Azure/AWS) to power client solutions.
Lead the build-out of AI-driven data infrastructure on Azure/Databricks, ensuring reliable pipelines for analytics and ML workloads using Spark/Scala.
Build and migrate ETL/ELT pipelines on Databricks Delta Lake and AWS, using PySpark, SQL, and AWS Glue to process batch and streaming data for a global fund-services provider.
Build and maintain scalable data pipelines and cloud infrastructure (GCP/AWS/Azure) to process large datasets, using SQL and Python for ETL and data quality monitoring.
Designs resilient, scalable data pipelines and products using Azure, SQL, Spark, and CI/CD to transform and deliver data for analytics and architecture.
Design and optimize Azure Synapse data pipelines and star schemas, tune performance, and build ingestion workflows using ADF and PolyBase for analytics workloads.
Build and maintain scalable data pipelines and infrastructure to collect, process, and analyze data for an iGaming company using Java/Scala, SQL/NoSQL, and Spark.
Build and optimize data pipelines using Microsoft Fabric, SQL Server, and Dynamics 365 to integrate and transform large datasets, then deliver insights via Power BI dashboards for business stakeholders.
Builds and maintains Azure-based data pipelines and ETL workflows using Azure Data Factory, Synapse, Databricks, and Spark, while ensuring data quality and performance.
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