Ajay Cheryala

Business Analyst

Hyderabad, Telangana, India5 yrs 10 mos experience

Highly Stable

Key Highlights

Increased team productivity by 8% through data-driven insights.
Automated workflows, saving significant time and effort.
Recognized as a top performer with multiple awards.

Stackforce AI infers this person is a Data-Driven Business Analyst in E-commerce, specializing in data analysis and process optimization.

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Skills

Core Skills

Data AnalysisBusiness Analysis

Other Skills

AnalyticsCommunicationSQLPythonData CleaningData VisualizationContinuous ImprovementData ProfilingDatabasesSQL Server Integration Services (SSIS)Team LeadershipData IntegrityLeadershipOperations ManagementTeam Management

About

As a Business Analyst at Amazon, I apply my data science skills and passion to enhance the e-commerce platform's performance. I have a Bachelor of Engineering in Mechanical Engineering from Swami Vivekananda Institute of Technology, and I have completed multiple certifications in SQL, Python, and Advanced Excel to strengthen my proficiency in data manipulation, extraction. In my role, I collaborate with cross-functional teams to validate and analyze data, identify patterns and trends, and generate insights and recommendations for process and program improvements. I have increased team productivity by 8%, received the Q2 2021 Cheetah Award and the Q1 & Q2 2022 Master Shifu Award, and been recognized as a top performer and a best process trainer. I enjoy working in a fast-paced and dynamic environment, where I can leverage data to solve real-world problems and create value for the customers and the business.

Experience

5 yrs 10 mos

Total Experience

5 yrs 10 mos

Average Tenure

5 yrs 10 mos

Current Experience

Amazon

Business Analyst

Jul 2020 – Present · 5 yrs 10 mos · Hyderabad · On-site

Extracted and analyzed data using SQL to deliver accurate, actionable insights to product managers.
Conducted A/B testing and data analysis to support product optimization and business decision-making.
Identified patterns and bottlenecks in audit data, improving team productivity by 8% through targeted process enhancements.
Automated repetitive workflows, reducing task execution time from 8 hours to 30 minutes and saving ~7.5 hours per day.
Revamped ACL feature with auto-fill functionality by integrating geo-code extraction, saving 20 hours per week per use case and enabling real-time updates.
Designed and implemented Python-based ETL pipelines to clean, consolidate, and aggregate SPL Weekly Business Review (WBR) data across multiple programs.
Reduced manual data processing effort by approximately 90% by replacing Excel-based workflows with reusable Python pipelines.

AnalyticsCommunicationSQLPythonData AnalysisData Cleaning+2