Lead Data Engineer with 9.5+ years of hands-on experience designing and scaling distributed data platforms and high-volume data pipelines across streaming, batch, and ML workloads. Strong expertise in Spark, Hadoop, Kafka, Kinesis, Databricks, and AWS, with a focus on performance tuning, cost-efficient compute/storage design, and production-grade pipeline reliability. Core strengths include: 1. End-to-end pipeline architecture & implementation — ingestion, storage, processing, orchestration, and consumption layers 2. Streaming & batch processing at scale using Spark, Kafka, Kinesis, AWS and Databricks 3. Cost optimization & workload performance engineering (query tuning, job right-sizing, storage lifecycle design) 4. Data quality, observability, and governance enablement across mission-critical datasets 5. Migration & modernization initiatives (on-prem → cloud, legacy ETL → Spark/Databricks, Qubole-> AWS) 6. ML feature engineering pipelines & model-serving data workflows in production environments 7. Close collaboration with Product, BI, and Data Science to translate analytical needs into scalable data solutions Known for building reliable, high-throughput pipelines and platform components with strong foundations in distributed systems, system design, data-intensive architecture principles, and Data structure & algorithms.