Praveen Kumar

• Project : Cargill Data Platform providing service to entire Cargill data
  products. Developing the data products logistics and production.
• Built hybrid cloud (AWS/Azure/GCP) data platforms, integrating IoT, ERP/CRM, and streaming data via Spark, Kafka, and cloud-native tools for real-time analytics and ML.
• Designed scalable data models (star/snowflake) in Snowflake, BigQuery, Redshift, and Synapse to power ML pipelines and dashboards.
• Automated ETL/ELT workflows with Airflow, Data Factory, and GCP Composer, enabling efficient data transformation for AI use cases.
• Enforced data governance using Databricks Unity Catalog and cloud tools (Purview, Lake Formation) for lineage, security, and compliance.
• Deployed ML models (forecasting, anomaly detection) via SageMaker,Vertex AI, and Azure ML, containerized with Docker/Kubernetes.
• Applied DevOps (Terraform, CI/CD) to automate infrastructure and pipeline deployments, improving team collaboration.
• Partnered with data scientists to operationalized models, monitor performance, and scale MLOps workflows.
• Mentored teams on PySpark/SQL best practices and data quality standards.

Tools : AWS/Azure/GCP, Snowflake, Databricks, Spark, Kafka, Airflow, Terraform, SageMaker, Docker.

Project: HPE Universal Internet of Things (IoT) Platform

• Led a team to design and deploy hybrid cloud (AWS, Azure, GCP) data pipelines for processing IoT telemetry data, enabling real-time analytics.
• Built a unified data lake architecture using Snowflake and Databricks to streamline data access and improve analytics efficiency.
• Developed scalable ETL/ELT pipelines with Apache Spark, Airflow,and PySpark , integrating diverse IoT device data for predictive maintenance workflows.
• Implemented data governance frameworks (GDPR, ISO 27001compliance) to ensure data accuracy and security across platforms.
• Automated cloud infrastructure and data workflows to optimize resource usage and reduce operational costs.
• Collaborated on deploying AI/ML models for anomaly detection, enhancing device reliability and operational insights.
• Mentored junior engineers on best practices, fostering team collaboration and timely project delivery.
  Tools : AWS/Azure/GCP, Snowflake, Databricks, Spark, Airflow, PySpark,
  Terraform, SageMaker, Unity Catalog.

• Optimized SQL queries across cloud and on-premise databases (Snowflake, BigQuery) to enhance performance, enabling faster analytics and decision-making.
• Built ETL pipelines (AWS Glue, Azure Data Factory) to ingest and transform large-scale datasets into centralized warehouses (Databricks, Snowflake), ensuring reliable data availability.
• Automated data workflows using shell scripting and orchestration tools (Airflow), reducing manual effort and improving operational efficiency.
• Collaborated with analytics and DevOps teams to clean, model (star schema), and interpret data, delivering actionable insights for business stakeholders.
• Enforced data governance (Unity Catalog, Apache Atlas) and security protocols (logging, audits) to meet compliance standards (GDPR) and ensure data integrity.
• Supported AI/ML initiatives by structuring datasets for model training (SageMaker, Azure ML) and integrating predictive outputs into business workflows.

Tools : AWS/Azure/GCP, Snowflake, Databricks, SQL, PySpark, Airflow, Unity Catalog, SageMaker.

• Contributed to all phases of the development lifecycle, from design to writing and developing.
• Designed, developed and tested Java-based solutions using common standards and frameworks for ease of maintenance.
• Automated repetitive tasks using Java, saving the team over 100 hours of manual work per month.
• Designed RESTful APIs for Java applications, enabling seamless integration with front-end technologies and third-party services.
• Applied Agile methodologies to manage Java development projects, improving team productivity and project delivery timelines.