Bala Snehith B.

About Bala Snehith B.

Senior Data Engineer with 5+ years of proven expertise in designing and optimizing enterprise-scale data pipelines across multi-cloud environments (AWS, GCP, Azure). Demonstrated success in processing 10M+ events daily with sub-second latency using advanced streaming technologies including Apache Kafka, Spark Streaming, and Apache Flink.Technical Leadership & Innovation: Architected petabyte-scale data solutions that reduced storage costs by 35% and improved dashboard performance by 60% through intelligent optimization strategies. Specialized in real-time recommendation systems, fraud detection pipelines, and MLOps workflows that drive measurable business impact—including 15% CTR improvements and 20% fraud reduction.Cross-Industry Expertise: Extensive experience across finance (Goldman Sachs), e-commerce (eBay), technology (Amazon), and healthcare (Abbott) sectors, with deep knowledge of regulatory compliance (SEC, FINRA) and security best practices. Successfully migrated legacy Hadoop infrastructure to modern cloud services, achieving 40% cost reduction while maintaining enterprise-grade performance.Agile Leadership & Delivery: Proven track record as Scrum Master with 95% sprint completion rates, leading cross-functional teams to deliver 12+ production features. Expert in CI/CD automation using Terraform, Jenkins, and GitLab CI, reducing deployment times by 25% and manual errors by 40%.Core Competencies: Python/PySpark, SQL optimization, Apache Airflow, Snowflake/BigQuery/Redshift, machine learning integration, and cloud-native architecture design.

Experience

1. Senior Data Engineer

   Goldman Sachs

   Aug 2024 — Present · NY, US

   Built mission-critical real-time trading infrastructure (Kafka, Flink, Spark Streaming) handling 10M+ events/day at <100 ms latency; supported $50B+ daily trading and risk analytics.• Saved $2M+ annually by engineering cost-efficient AWS architecture (S3 tiering, compression, EMR optimization), cutting storage costs 35% and scaling to petabyte-level workloads.• Accelerated C-suite risk decisions by 60% via Snowflake performance tuning (partition pruning, clustering keys) that reduced dashboard load times.• Boosted algorithmic trading profitability by delivering real-time ML feature pipelines (PySpark, Pandas, scikit-learn) improving model accuracy 15%.• Reduced production deployment risk 40% with end-to-end IaC (Terraform, Jenkins, Airflow); ensured SEC/FINRA compliance with zero violations.

Education

• Geethanjali College of Engineering and Technology

  Bachelor of Technology - BTech, Electrical, Electronics and Communications Engineering

  2016 — 2020

• University of Massachusetts Lowell

  Master of Science - MS, Computer Science