GOPI KUNCHALA
Results-driven Data Engineer with 3.4 years of experience specializing in scalable Azure cloud platforms, Databricks PySpark pipelines, and high-performance SQL query tuning. Proven delivery record of 60+ critical RCAs and 70+ permanent problem fixes.
[INF] Databricks Spark Engine: Delta Lake Active
[INF] Azure Data Factory: Scheduled Batch Pipeline Executing
[INF] ADLS Gen2 Ingestion: Multi-Terabyte Migration (100% Integrity)
[OK] PySpark Transformations: Delta & Snowflake Tables Populated
[INF] SLA Impact: 80% Error Trend Reduction | 90% Incident Drop
[SUCCESS] 30+ Safe Deployments successfully committed._
Production Reliability & SLA Timeline
Detailed root cause investigation reports with zero recurrence.
Permanent defect investigations and fixes across pipelines.
Pandas & Control-M automated regular data task workflows.
Zero-downtime production code & configuration releases.
Standardized incident process, eliminating recurring issues.
No-data-loss ingestion across multi-terabyte migrations.
About & Engineering Philosophy
Gopi Kunchala
Enterprise Scalability & Resilience
Results-driven Data Engineer with 3.4 years of enterprise production experience specializing in Azure cloud platforms, ETL/ELT pipeline engineering, and data platform performance optimization. Proficient in PySpark, SQL tuning, and Python for building scalable data solutions.
Strong expertise in Azure Data Factory, Databricks, and Snowflake with a proven track record of optimizing data processes and implementing automation frameworks. Microsoft Azure and Databricks certified professional with deep problem-solving skills and the ability to operate safely in high-stakes enterprise production environments.
Core Competencies & Tooling
Enterprise technologies mastered and operated in daily production.
Data Engineer
Delivered 60+ various Root Cause Analyses (RCA) for critical production issues accompanied by detailed technical investigation reports.
Completed 70+ problem investigations and implemented permanent fixes to resolve complex data pipeline challenges.
Executed 30+ safe production deployments including both code fixes and configuration changes with zero downtime.
Optimized SQL queries to improve performance and reduce execution time across production data pipelines.
Developed PySpark and SQL notebooks to implement smooth data transformations for Snowflake and Delta tables loading.
Led cross-team platform initiatives to implement cloud-based Control-M level automation and job scheduling.
Implemented code fixes in shell scripts to resolve automation issues and improve platform reliability.
Developed large file handling solutions in ADLS Gen2 for efficient renaming and data management.
Introduced pandas-based workflows to generate automated solutions for regular data tasks (applied 5+ ETL automations).
Analyzed critical production issues and resolved complex data load challenges within quicker SLA timeframes to maintain stability.
Reduced production errors by 80% in weekly trend analysis through root cause analysis and permanent fixes for recurring issues.
Standardized incident management process, achieving a 90% reduction in recurring production incidents.
Developed mount issue error fixes to prevent recurring Databricks mount failures and ensure high data availability.
Accelerated large file data movement within Databricks mounts, enabling efficient migration of multi-terabyte datasets.
Implemented no-data-loss ingestion framework ensuring 100% data integrity during large-scale enterprise migrations.
Cloud Architecture & Engineering Badges
Industry-recognized professional cloud engineering credentials.
Azure Data Engineer Associate
Data Engineer Associate
Azure Fundamentals
Lakehouse Platform Fundamentals
Educational Qualifications
Mechanical engineering, quantitative analysis, and systems analytical foundations applied to enterprise cloud big data pipelines.
Bachelor of Technology (B.Tech)
Diploma
Get in Touch & Connect
Open for enterprise cloud data engineering roles, large-scale lakehouse migrations, and consulting engagements.
