Sreesha V

Data Engineer with 1+ year of hands-on experience designing, building, and maintaining ETL pipelines on Azure Databricks using Python, SQL, PySpark, and Spark SQL. Skilled in data transformations, data quality engineering, and structuring data across Bronze, Silver, and Gold layers using Medallion Architecture to deliver accurate, analytics-ready datasets. Experienced in translating source-to-target mapping (STTM) specifications into Python/Spark logic, building metadata-driven automation frameworks that reduced implementation time by 30%, and implementing data validation, reconciliation, and governance controls.

Proficient in Git-based version control, CI/CD, and Azure DevOps for reliable, collaborative pipeline development and deployment.

Databricks Certified Data Engineer

Associate, with strong analytical skills and a track record of improving data pipeline performance and reliability across structured and semi-structured data.

Data Engineer, Full-time - Mathco - Bangalore, India

(2025-09 - 2026-06)

• Designed, built, and maintained scalable ETL pipelines in Azure Databricks using Python, PySpark, and Spark SQL, translating source-to-target mapping (STTM) specifications into transformation logic and structuring data across Bronze, Silver, and Gold layers (Medallion Architecture) to deliver accurate, analytics-ready datasets end-to-end.
• Engineered a metadata-driven automation framework in Python to auto-generate pipeline files, processes, and functions directly from STTM specifications, reducing overall implementation time by 30% and establishing reusable patterns and best practices across data pipeline delivery.
• Performed data transformations and implemented data quality, validation, and reconciliation checks using Spark SQL to certify source-to-target data load accuracy and strengthen governance and audit readiness.
• Managed version control and CI/CD workflows for Databricks notebooks using Git and Azure DevOps branching strategies, supporting reliable, automated, and collaborative pipeline development through deployment.
• Collaborated with analytics, platform, and business stakeholders in Agile/Scrum environments to gather data requirements and deliver reliable, well-structured data solutions.

Data Engineer Intern - Mathco - Bangalore, India

(2025-01 - 2025-06)

• Designed and developed an enterprise-grade Data Quality Engine on Azure Databricks using PySpark, automating end-to-end data quality monitoring, validation, and reconciliation across multiple structured and semi-structured datasets at scale.
• Architected a metadata-driven framework that dynamically retrieved validation rules and configuration mappings, enabling scalable, reusable data quality checks across diverse pipelines with minimal code changes — supporting shared data standards and control mechanisms across the analytics organization.
• Built modular PySpark notebooks to execute SQL-based validation rules, enforce data governance standards, and perform data profiling and root-cause analysis to improve data reliability for downstream analytics and reporting.
• Generated data quality reports and metrics dashboards, delivering actionable insights into validation results, anomaly detection, and overall data health to support efficient debugging and stakeholder decision-making.

B.E. - Computer Science Engineering - Sri Venkateswara College of Engineering (2021-09 - 2025-05)