Sudeep Govathoti

About Sudeep Govathoti

Data Engineer with 4+ years building production data platforms on Microsoft Azure (Synapse, Data Factory, Databricks, ADLS) and AWS (S3, Redshift) using Python, PySpark, SQL, dbt, and Apache Airflow. I specialize in dimensional modeling, incremental loads, partitioning strategy, schema evolution, data contracts, and CI/CD for pipelines — shipping observable data products backed by validation frameworks, pipeline monitoring, and clear data governance that unblock analysts and power executive-ready Power BI and Tableau reporting. What I\'ve delivered recently: • Cut pipeline failures by 40% and improved data accuracy by 30% through incremental loads and partitioning on Azure Synapse + Redshift. • Reduced pipeline runtime by 35% and cloud compute spend by ~20% via Spark tuning (partitioning, bucketing, broadcast joins, caching). • Cut time-to-detection of upstream data issues by 50% with anomaly detection and data observability patterns (alerts, freshness SLIs). Currently: Pursuing Azure Data Engineer Associate (DP-203), AWS Data Engineer Associate, and Databricks Data Engineer Associate certifications. Open to: Data Engineer / Analytics Engineer roles in Charlotte, NC (remote / hybrid / relocation). s••••••••@gmail.com

Experience

1. Data Engineer

   Verizon

   Jul 2024 — Present

   Cloud data pipelines on Microsoft Azure (Synapse Analytics, Data Factory, Databricks, ADLS Gen2) and Amazon Redshift for marketing, web analytics, and customer-behavior reporting.• Built end-to-end cloud data pipelines ingesting marketing, web-analytics, and customer-behavior data from 10+ enterprise sources into Azure Synapse Analytics and Amazon Redshift; incremental loads and partitioning strategy improved data accuracy by 30% and cut pipeline failures by 40%.• Developed ETL/ELT workflows in Azure Databricks using PySpark, Apache Spark, Python, and SQL, processing terabyte-scale datasets with aggregations, deduplication, and schema-evolution handling into curated Parquet data products for 4 downstream BI teams.• Automated orchestration with Azure Data Factory (ADF) and Apache Airflow DAGs; introduced CI/CD for data pipelines via Azure DevOps, eliminating 30% of manual intervention and tightening SLA adherence for operational and regulatory reporting.• Modeled star-schema data marts (fact + dimension tables) in Azure Synapse powering Power BI dashboards for marketing ROI, customer behavior, and operational KPIs; unlocked self-service analytics for 50+ users and saved ~15 analyst-hours per week.• Established data quality checks, data validation frameworks, data contracts, and pipeline monitoring with anomaly detection and data observability patterns (alerts, freshness SLIs), cutting time-to-detection of upstream issues by 50% and strengthening data governance and lineage.• Tuned Apache Spark jobs and SQL queries via partitioning, bucketing, broadcast joins, and caching, reducing pipeline runtime by 35% and cloud compute costs by ~20%.• Partnered with analysts, marketing, product, and platform engineers; rotated on-call to triage and resolve production incidents under strict SLAs.Tech: Python, PySpark, Apache Spark, SQL, Azure Synapse, Azure Data Factory, Azure Databricks, ADLS Gen2, Amazon Redshift, AWS S3, Power BI, Git, CI/CD, Azure DevOps, Agile/Scrum.

Education

• Lindsey Wilson University

  Master's degree

• KL University

  Bachelor's degree, Computer Science