Ramdas Vk
@Ramdas0716
Data Engineer at Tredence Analytics
Bangalore, Karnataka, India
Data Engineer with 3 years of experience building large-scale batch and streaming data pipelines on cloud platforms. Experienced in Python, SQL, and PySpark with hands-on expertise in distributed data processing, lakehouse architectures, and real-time data systems on GCP and Azure. Proven track record of building scalable data infrastructure processing millions of records daily.
Experience
Data Engineer
Tredence Analytics
Enabled processing of 8M+ events per day by designing and implementing a scalable Azure Lakehouse architecture (Bronze–Silver–Gold) using ADLS Gen2 and Delta Lake for analytics pipelines. Ingested and processed 10M+ wearable telemetry records daily by building batch and incremental ingestion pipelines using Azure Data Factory, Event Hubs, PySpark, and Spark SQL on Azure Databricks. Reduced Spark job runtime and compute costs by ~25% while improving query performance by 35% by optimizing distributed workloads using partitioning, caching, and efficient join strategies. Improved pipeline reliability and automated deployment of 10+ production pipelines by implementing data quality validation, schema evolution handling, and CI/CD workflows using GitHub and Azure DevOps.
Education
Sri Chandrasekharendra Saraswati Viswa Maha Vidyalaya
Bachelor of Engineering
Licenses & Certifications
Associate Data Practitioner
Google Cloud