Sai teja Nagulla

Results-driven Data Engineer with 3+ years of IT experience specializing in Azure Databricks, Apache Spark, and Delta Lake for building scalable, cloud-native data pipelines. Proven expertise in PySpark-based ETL development, data migration, performance tuning, and enterprise reporting using Power BI. Experienced across Automotive and Telecommunications domains with a strong focus on data quality, Lakehouse architecture, and business-critical analytics.

Data Engineer - Intellinet Systems - Gurugram, India

(2025-07)

Leading Azure Databricks-based data engineering initiatives for Escorts Kubota's enterprise automotive transformation platform.

• Building and orchestrating scalable PySpark ETL pipelines on Databricks with Delta Lake for ACID-compliant, versioned data management.
• Executing large-scale data migrations from legacy platforms to EPC systems with automated validation, consistency, and integrity checks.
• Developing and optimizing complex data transformation logic and database objects for business-critical automotive workflows.
• Building Power BI dashboards integrated with Databricks SQL endpoints for real-time operational and management reporting.
• Collaborating with cross-functional teams via JIRA to resolve production issues and deliver high-priority data engineering solutions.

Data Engineer - FIS Global - Dallas, TX

(2023-02 - 2025-07)

• Designed and maintained scalable ETL pipelines using Spark, Airflow, and SQL to process trading, client, and market data into AWS S3, powering daily NAV reporting, portfolio analytics, and compliance workflows.
• Collaborated with investment, compliance, and risk teams to deploy machine learning models for churn prediction and settlement failure risk, improving forecast accuracy by 28% and reducing reconciliation time.
• Implemented executive-ready Power BI dashboards to monitor fund performance, trade exceptions, and regulatory KPIs, enabling faster insights for portfolio managers and financial advisors.
• Led Agile sprints and CI/CD automation using GitHub Actions and Docker, streamlining deployment cycles and ensuring high availability of data pipelines across multiple business units.
• Developed fraud detection and recommendation models using Python, Scikit-learn, and TensorFlow, improving detection accuracy by 22% and reducing false positives by 15%.
• Designed and deployed REST APIs to expose analytics features to internal teams, reducing engineering dependencies and cutting ad-hoc query volume by 35%.
• Performed root cause analysis for data anomalies in production pipelines, resolving quality issues within 4-6 hours on average and reducing report disruptions.
• Architected a migration of 20TB+ of financial workloads to ATLAS with 100% integrity, developing automated validation frameworks to resolve 99% of data discrepancies for security auditing.
• Refactored PySpark ETL pipelines to process billions of records daily, optimizing compute efficiency by 30% and ensuring zero production downtime for mission-critical financial systems.
• Built and maintained automated ETL pipelines using Airflow and AWS Redshift to support structured, timely access to customer data, reducing data latency by 35%.

Master of Science - Computer Science - University of North Texas

Bachelor of Technology - Computer Science & Engineering - Lovely Professional University