AWS Cloud

Configure / deploy AWS stack and build AWS Data Pipeline for data loads
Build Docker container clusters managed by Kubernetes and Docker on AWS
Deploy AI microservices in production on AWS
Develop and optimize PySpark jobs within AWS Glue to transform and process large-scale datasets
Develop data pipelines to move data to snowflake / seamless data loading

Azure Cloud

Implement Azure data solutions
Create pipelines in Azure Data Factory to extract, transform and load data from sources
Migration of onPrem to Azure Cloud using Azure Data Factory
Integrate Kubernetes with network, storage and security to provide comprehensive infrastructure and orchestrated containers across multiple hosts
Design, implement & manage Kubernetes Clusters
Implement BI solutions on Azure cloud using Azure Data Platform Services and Databricks

Databricks

Develop Spark applications in Databricks for data extraction, transformation and aggregation from multiple sources
Build processes/data pipelines that syncs different data in real-time
Ingest various data sets from multiple data sources into data lake for downstream applications and analytics
Administration, configuration and optimization of the Databricks platform
Install, configure and maintain Databricks clusters and workspaces
Administer interfaces with Azure AD and Amazon AWS

Snowflake

Setup and configure Snowflake Data Warehouse
Design, implement data models and schemas
Develop data ingestion pipelines for data transfer from sources to Snowflake
Migrate data from legacy database to Snowflake
Tuning of Snowflake queries and data processing tasks to optimize query execution time and resource utilization
Develop Python scripts to ensure data accuracy and consistency within Snowflake

Palantir

Develop and automate data integration workflows within Palantir Foundry to ingest, transform and integrate data from disparate sources
Design, implement data models & schemas within Palantir Foundry for analysis and visualizations
Monitor the performance of Palantir Foundry deployments

Cloudera

Data modelling & pipeline development with Spark, Hive
Real-time streaming architecture (Apache NiFi, Kafka)
Model training and deployment via Cloudera ML
Migration from legacy Hadoop distributions
AI/ML integration with CML or third-party tools

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