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Designs and leads enterprise-scale AI and cloud data platforms for an airline industry client, leveraging Azure services like Databricks, OpenAI, and Delta Lake.
Designs and leads enterprise data strategy, cloud modernization, and GenAI initiatives using Azure, lakehouse architecture, and RAG systems to drive digital transformation.
Lead enterprise data engineering and analytics teams to design scalable data platforms using Azure, Databricks, and Power BI for executive reporting and digital transformation.
Design and own enterprise-scale data architectures for a cloud-native lakehouse, defining models, governance, and integration patterns that support real-time and AI workloads in a high-governance environment.
Designs and leads enterprise-scale cloud data platforms and AI systems for an airline, focusing on Azure, GenAI, and modern data architecture.
Builds and deploys production-grade AI, ML, generative AI, and computer vision systems for a hospital, integrating with EHR, imaging, and clinical workflows while ensuring safety and scalability.
Designs and optimizes scalable data platforms using Python, PySpark, Databricks, AWS, and SQL to build batch/real-time pipelines and lakehouse solutions.
Build and optimize scalable data pipelines on Databricks and AWS using Python, PySpark, and SQL for enterprise clients.
Build and optimize Databricks pipelines using Delta Live Tables and PySpark, migrate legacy systems to modern CI/CD, and ensure output equivalence with Palantir Foundry.
Design and maintain ETL/ELT pipelines using Informatica Cloud to integrate and transform enterprise data from diverse sources for analytics and reporting.
Build and optimize cloud data pipelines in Snowflake, orchestrate workflows with Airflow, and transform data using dbt to support enterprise analytics.
Design and build scalable data pipelines using Python, PySpark, Databricks, and AWS for enterprise clients.
Principal Data Engineer builds and scales real-time and batch data pipelines using PySpark, Kafka, Flink, and cloud orchestration tools, while leading a team to deliver robust analytics infrastructure.
Lead design and implementation of real-time and batch data pipelines using PySpark, Kafka, and Azure, while optimizing lakehouse storage and mentoring engineers.
Lead the design, governance, and scaling of a data lakehouse (SAP BDC/Databricks) and deliver predictive analytics/AI use cases to drive business decisions.
Lead the design and implementation of enterprise data platforms, data architecture, and BI systems for a pharmaceutical company, ensuring scalable, secure, and high-performing solutions.
Designs and integrates AI/ML solutions into a data lakehouse using Spark, Kafka, and Jupyter Enterprise Gateway for batch and real-time inference.
Designs and architects AI/ML solutions integrating batch and real-time inference using Spark, Kafka, and Jupyter Enterprise Gateway within a data lakehouse.
Build and migrate data pipelines from Hadoop to a modern lakehouse using Spark 3 and Iceberg, ensuring reliable reporting and analytics for core business needs.
Design and build production pipelines to migrate a legacy Hadoop warehouse to a modern lakehouse using Spark, Iceberg, and Airflow for core analytics.
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