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Design and build scalable data platforms, implement ETL/ELT pipelines, and collaborate with data scientists to make data actionable using SQL, Python, and AWS.
Lead Data Engineer designs and optimizes scalable big-data pipelines and architectures for clients, using Spark and Kubernetes.
Build and optimize cloud-based data pipelines and modern data platforms, working with Data Scientists to enable scalable, secure data workflows using SQL, Python, and a major cloud provider.
Designs, builds, and optimizes cloud data pipelines using Azure Data Factory, AWS Glue, or GCP Dataflow with Python, Spark, and SQL.
Lead Data Engineer designs and scales Big Data pipelines and cloud infrastructures for clients, using Spark, Kafka, Kubernetes and DevOps practices to ensure high-performance, scalable data processing.
Build and scale cloud data pipelines for clients using Azure, AWS, or GCP, ingesting and transforming data with Spark, Airflow, and Kafka to make it analytics-ready.
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