Sushant Kumar

• Drove $600 million revenue initiative by architecting customer onboarding orchestrator during Teradata to GCP migration.
• Led 20-person data engineering team to integrate over 30 security detectors, optimizing data flow on Google Cloud Platform.
• Scaled Security Command Center for real-time ingestion of over 50 million asset telemetry records for more than 33,000 customers.
• Architected high-volume data pipelines with a team of 12 engineers, establishing critical platform reliability standards.
• Spearheaded Error Detection and Data Quality & Anomaly Framework, generating over $1 million in savings for 350+ premium customers within first quarter.
• Designed pay-as-you-go analytical model to unlock market insights, expanding reach to SMB customers.
• Delivered Public Configuration API that streamlined security configurations and enhanced customer onboarding processes.
• Developed cloud-native analytics platform for global manufacturing client by integrating ERP data from SAP S/4HANA into Azure Data Lake.

• Utilized PySpark, Kafka, AWS, and Azure in Big Data development for one year.
  Executed multiple modules of data engineering pipelines and complete ETL processes.
  Employed data lake and data warehousing strategies to enhance data accessibility.
  Designed and implemented data models to support business intelligence initiatives.
  Leveraged Python, SQL, and dbt for data transformation and analysis.
  Integrated PowerBI for visualizing complex datasets and generating insights.
  Managed workflows using Airflow to automate tasks and improve efficiency.
  Analyzed data with Looker to deliver actionable reports and dashboards.

• Worked on Integration of Speedway Migration of data with Seven Eleven.
• Lead a Team of ML/Data Engineers with Personalization/Inventory Management/Item Master/ML initatives succesfully.
• Proposed multiple New ideas to Bring Freshness in Work to GSC Bengaluru.
• Developed a Universal Semantic layer using AtScale, saving $1.2 million by optimizing data access (Tabular and Multi-Dimensional Cube)
• Tech Stack:Data lake, Data Warehousing, Data Modeling, Data Design, Python, AWS, Azure, SQL, dbt, PowerBI, Airflow ,Looker,DataBricks.

• Utilized PySpark, Kafka, AWS, and Azure to develop and optimize ETL processes over two years.
  Designed and implemented Big Data pipelines for batch processing and real-time streaming solutions.
  Processed complex data using Spark Datasets from sources including HDFS, Amazon S3, ADLS Gen1, and ADLS Gen2.
  Executed real-time stream analytics on large datasets in AWS environments.
  Demonstrated proficiency in data lake architecture, data warehousing, and data modeling techniques.
  Employed Python, SQL, dbt, PowerBI, Airflow, and Looker in various projects.

• Accumulated one year of experience as Big Data Developer utilizing PySpark, Kafka, AWS, Azure, and Java.
  Developed and maintained multiple modules within data engineering pipelines and complete ETL processes.
  Designed data lakes and data warehousing solutions to enhance data accessibility.
  Executed data modeling and design strategies to support business intelligence initiatives.
  Leveraged Python, SQL, dbt, and PowerBI for efficient data analysis and reporting.
  Utilized Airflow for orchestrating complex workflows and ensuring data pipeline reliability.
  Implemented Looker for data visualization, facilitating insights generation for stakeholders.

Project: AI-Powered Manufacturing Insights on AWS and Databricks Lakehouse

Led the design and implementation of a scalable AI-assisted BI solution for a global manufacturing client using AWS and Databricks. Built a robust data lakehouse architecture with Delta Lake and Apache Spark, integrating high-volume data from SAP S/4HANA, SAP BW, and BODS using AWS Glue, DMS, and Lake Formation. Developed efficient ETL pipelines in PySpark and SQL to support real-time and batch analytics across production, inventory, and quality control use cases. Enabled streaming ingestion via Kinesis, reducing operational reporting delays by 60%. Managed cloud costs through optimized EMR and S3 configurations. Deployed models using MLflow for predictive maintenance. Demonstrated strong leadership by mentoring junior engineers, coordinating across cross-functional teams, and delivering within tight timelines. The project resulted in improved decision-making, reduced downtime, and empowered business users with self-serve analytics dashboards, leveraging Power BI and Databricks SQL.