VENKATA SUDHEER NAGOTHU
Chennai
Summary
Results-driven Data Engineer with 3.6 + years of experience in Data Engineering, Business Intelligence, Big Data processing, and DevOps automation. Expertise in Azure Cloud, SQL Server, SSIS/SSAS, ADF, Databricks, PySpark, Power BI, and CI/CD. Proven track record in building scalable data pipelines, optimizing ETL performance, automating deployments, improving data quality, and enabling business-driven analytics. Skilled in cross-functional collaboration and delivering high-impact reporting solutions for enterprise-level applications. Results-focused data professional equipped for impactful contributions. Expertise in designing, building, and optimizing complex data pipelines and ETL processes. Strong in SQL, Python, and cloud platforms, ensuring seamless data integration and robust data solutions. Known for excelling in collaborative environments, adapting swiftly to evolving needs, and driving team success.
Overview
3
3
years of professional experience
Work History
Data Engineer / MSBI Developer
HDFC Bank
08.2022 - Current
- Designed and implemented high-performance ETL pipelines using Azure Data Factory & SSIS to ingest structured and semi-structured data into Azure Data Lake & Azure SQL DW.
- Built scalable PySpark & Azure Databricks workflows processing large datasets (millions of records daily) for advanced analytics.
- Developed SSAS Tabular/Cubes and Power BI dashboards, enabling multi-dimensional data analysis and real-time reporting for senior leadership.
- Automated data pipeline deployment and environment promotion using Git, PowerShell & CI/CD (Jenkins / Azure DevOps), reducing manual release time by 60%.
- Optimized SQL queries, indexing and data warehouse models reducing processing time by 60% and improving reporting performance.
- Created C#/.NET backend modules interfacing internal applications and databases to streamline business operations.
- Delivered SQL Server database performance tuning, maintenance and backup automations.
- Built automated ETL monitoring logs and PowerShell-based maintenance scripts reducing manual reporting workload by 70%.
- Collaborated closely with business users to define data transformation rules and reporting KPIs.
Education
Master of Science - Statistics
Pondicherry University
Puducherry05-2022
Bachelor of Science - Mathematics, Statistics & Computer Science
Krishna University
Machilipatnam, Andhra Pradesh05-2020
Intermediate - Board of Intermediate Education, AP
AP Model School
Duttalur05-2017
Skills
- Cloud & Data Platforms - Azure Data Factory, Azure Data Lake (ADLS), Azure Databricks, Azure Synapse, Azure SQL / SQL DW
- Databases & ETL / BI - SQL Server (T-SQL), SSIS, SSAS, Data Warehousing, Data Migration, Data Modeling
- Big Data & Spark Ecosystem - PySpark, Apache Spark, Databricks notebooks, Large-scale pipelines
- Programming / DevOps / Automation - Python, C#, NET, PowerShell, Git, Jenkins, CI/CD, Version Control
- Reporting & Visualization - Power BI, Excel (Pivot/Power-Pivot), Dashboarding, OLAP Analytics
Languages
English
Bilingual or Proficient (C2)
Tamil
Intermediate (B1)
Telugu
Bilingual or Proficient (C2)
Hindi
Intermediate (B1)
Timeline
Data Engineer / MSBI Developer
HDFC Bank
08.2022 - Current
Master of Science - Statistics
Pondicherry University
Bachelor of Science - Mathematics, Statistics & Computer Science
Krishna University
Intermediate - Board of Intermediate Education, AP
AP Model School
PROJECT HIGHLIGHTS
PAN-India Telecalling Performance Analytics Platform — Built end-to-end Azure-based pipeline (ADF + Databricks + PySpark + ADLS + Azure SQL) processing 25M+ records/month, enabling insights into conversions, productivity and quality metrics.
Centralized Customer 360 Insights Dashboard — Integrated CRM & telecalling datasets improving lead prioritization accuracy by 40% using Power BI analytics.
Automated Lead Allocation & Tracking System — ADF + Databricks-based automation improving call-to-conversion ratio by 30% across digital and branch channels.
Call Quality & Productivity Monitoring Framework — Power BI + SQL-based monitoring across 15,000+ telecallers, reducing reporting time by 70% and improving SLA compliance.
Customer Segmentation & Conversion Prediction Model — ML-assisted segmentation using Databricks + PySpark, achieving 18% improvement in telecalling conversions.
Automated ETL & CI-CD Deployment System — Automated ADF deployments via Git + Jenkins reducing deployment time from hours to minutes.
Data Quality Framework for Telecalling Campaign Data — Achieved 99.5% accuracy through validation rules, exception handling and PowerShell logging.