Sai Praneeth
Data Engineer @Comcast
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WORK HISTORY
Data Engineer @Comcast
Architected and deployed a real time streaming pipeline handling over 50M+ events daily using Pub/Sub, Dataflow,and Databricks, improving operational analytics latency from minutes to seconds.• Developed end to end machine learning data pipelines integrating with Vertex AI and MLflow, reducing model trainingtime by 40% and improving prediction accuracy by 18%.• Designed data quality and observability framework in Python, implementing anomaly detection, schema validation,and automated rollback logic, achieving 99.9% data reliability.• Migrated legacy ETL workloads from on prem to GCP BigQuery, optimizing query execution time by 70% and saving$250K annually through cost optimization strategies.• Partnered with Data Science and Business Intelligence teams to create feature stores that streamlined ML modeldeployment cycles by 35%.• Introduced dbt for modular transformations and lineage tracking, enabling reusable datasets and faster featureengineering.• Deployed CI/CD pipelines using GitHub Actions and Terraform, automating infrastructure provisioning and Airflow DAGdeployments.• Implemented data catalog and access controls improving governance, compliance, and audit traceability acrossbusiness units.
EDUCATION
Indiana Wesleyan University
Master's degree, Information Technology
MVSR Engineering College
Bachelor's degree, Computer Science
ABOUT SAI PRANEETH
Detail-oriented and results-driven Data Engineer with 6+ years of experience designing, building, and optimizing scalable data solutions across cloud platforms. Skilled in Python, SQL, BigQuery, Apache Airflow, and Snowflake, with a proven track record of delivering robust ETL/ELT pipelines, real-time analytics, and automated workflows that reduce costs and improve operational efficiency.Proficient in collaborating with cross-functional teams to bridge the gap between technical execution and business objectives, enabling data-driven decision-making and measurable business outcomes. Adept at implementing data governance, monitoring frameworks, and cost optimization strategies for cloud platforms.Key Strengths- Cloud Data Warehousing & Big Data Processing (BigQuery, Snowflake, Dataflow, Spark)- ETL/ELT Pipeline Design & Workflow Automation (Airflow, Cloud Composer, Python, SQL)- Real-Time Data Streaming & Analytics (Pub/Sub, Kafka, Dataflow)- Data Governance, Quality Frameworks & Cost Optimization- Visualization & Reporting (Tableau, Looker, Google Data Studio)
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