Aditya Yandrathi

12 years of experience in Data Engineering with deep expertise in Databricks, Snowflake, and dbt — building scalable data pipelines, cloud lakehouses, and enterprise data warehouses across AWS, Azure, and GCP. Proficient in end-to-end ETL/ELT design, cloud migrations, and real-time data processing. Adept at collaborating with global clients and leading cross-functional teams to deliver high-quality, production-ready solutions. Core stack centers on Databricks (Delta Lake, DLT, MLflow, Spark Structured Streaming), Snowflake (Snowpark, Streams & Tasks, Cortex AI, query optimization), and dbt (Core, Cloud, testing frameworks) — consistently delivering 30–45% performance improvements across financial, CPG, and healthcare verticals. Strong AWS depth: Glue, EMR, Redshift, Kinesis, Lambda, LakeFormation, and SageMaker. Operates as an AI-native engineer — uses Claude Code and agentic workflows to compress multi-week migration and pipeline delivery efforts, accelerating dbt model development, Snowflake optimization, and Databricks pipeline automation with 5–10× throughput gains. Proven track record migrating on-premise workloads to cloud-native architectures using AWS DMS, Schema Conversion Tool, and CDC patterns. Experienced in implementing MLOps pipelines with SageMaker, Vertex AI, and DataRobot, and enforcing data governance frameworks compliant with GDPR, HIPAA, and SOX across multi-cloud environments.