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Designs and optimizes scalable data pipelines using Snowflake and cloud environments, focusing on data warehouse lifecycle and engineering best practices.
Lead a team of data engineers to design and implement high-impact data architectures and solutions for enterprise clients using modern cloud platforms and AI tools.
Builds and maintains cloud data pipelines on GCP (Dataflow, Airflow, BigQuery) and SQL databases to feed data warehouses for business analytics.
Designs and builds dbt models and optimizes Snowflake data pipelines for analytics in a hybrid work setup.
Designs and optimizes cloud data pipelines using Snowflake, dbt, and advanced SQL for international projects, implementing ELT architectures and automated testing.
Build and maintain data pipelines using Microsoft’s stack (Azure Data Factory, Synapse, Fabric) to integrate and transform data for analytics and AI solutions.
Build and maintain large-scale data pipelines using Python and PySpark while helping design modern data architectures for high-impact projects.
Builds end-to-end data pipelines on Databricks using Python and PySpark to process large-scale datasets in a distributed Spark environment.
Build and optimize Snowflake data pipelines and dbt models for analytics, applying ELT, Kimball modeling, and CI/CD practices in a cloud environment.
Build and run end-to-end data pipelines for enterprise clients, designing ETL workflows, cloud data models, and dashboards while mentoring junior engineers.
Build and optimize cloud data pipelines using GCP services like Dataflow, BigQuery, and Dataproc to help clients process and analyze large datasets efficiently.
Build and deploy data pipelines, ETL workflows, and cloud data warehouses for enterprise clients using SQL, AWS/GCP/Azure, and visualization tools.
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