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Maryville
Bharadwaj Bairi

Bharadwaj Bairi

Data Engineer

Technology / Internet

Maryville, Township of Polk, Nodaway

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About Bharadwaj Bairi:

  • Cloud Data Engineer with 3+ years of experience building scalable ETL pipelines across AWS and GCP using Python, PySpark, Airflow and AWS Glue.
  • Skilled in data warehousing with Snowflake, BigQuery, Redshift, and pipeline optimization through query tuning and cost reduction.
  • Hands-on with Docker & Kubernetes for container orchestration; implemented CI/CD pipelines using Git, Jenkins, and Terraform.
  • Developed real-time streaming pipelines using Kafka, Kinesis, Lambda, and Spark Streaming; migrated legacy SQL/Oracle systems to modern cloud warehouses.
  • Designed fault-tolerant, monitored workflows using CloudWatch and CloudTrail; built automated reporting dashboards using QuickSight and BigQuery.
  • Collaborated cross-functionally with analysts, architects, and DevOps to ensure scalable, business-aligned data solutions.

Experience

  • Cloud Data Engineer with 3+ years of experience building scalable ETL pipelines across AWS and GCP using Python, PySpark, Airflow and AWS Glue.
  • Skilled in data warehousing with Snowflake, BigQuery, Redshift, and pipeline optimization through query tuning and cost reduction.
  • Hands-on with Docker & Kubernetes for container orchestration; implemented CI/CD pipelines using Git, Jenkins, and Terraform.
  • Developed real-time streaming pipelines using Kafka, Kinesis, Lambda, and Spark Streaming; migrated legacy SQL/Oracle systems to modern cloud warehouses.
  • Designed fault-tolerant, monitored workflows using CloudWatch and CloudTrail; built automated reporting dashboards using QuickSight and BigQuery.
  • Collaborated cross-functionally with analysts, architects, and DevOps to ensure scalable, business-aligned data solutions.

Education

  • Built and optimized data pipelines using Apache Spark, Hive, Kafka, MySQL, and HBase; improved processing efficiency by 30% through Spark-Hive and Spark-HBase integration and 5x faster performance via Spark job tuning.
  • Automated data transfers using Sqoop and Airflow, reducing MySQL-to-Hive sync time by 2x; developed scalable ingestion workflows using AWS Glue, Lambda, and S3, maintaining 99.8% uptime across batch and streaming pipelines.
  • Designed and deployed real-time streaming applications using Kinesis and Lambda, reducing data lag by 40%; implemented cost-effective storage strategies using S3 lifecycle policies.
  • Deployed containerized ETL workflows on AWS ECS and Fargate for high availability; configured real-time monitoring and alerts for 15+ pipelines using CloudWatch and CloudTrail.
  • Integrated Snowflake as a centralized cloud data warehouse; built ingestion pipelines from S3 and Kafka using Airflow and Python, and optimized queries and warehouse sizing to reduce compute costs by 25%.

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