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Business Intelligence Engineer with 5+ years in Data Analysis & AI Tools
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Data and Analytics professional with close to 5 years of experience at Amazon, where the work has always been fundamentally analytical - querying data, tracking KPIs, identifying patterns in large case volumes, and turning findings into decisions that leadership can act on. On the technical side, I work with Python (Pandas, EDA), SQL, and Advanced Excel for analysis, and I've started building with Power BI for reporting. I also use AI tools including ChatGPT in my analytical workflow - for structuring analysis, drafting prompts for data tasks, and accelerating research.
JNTU Hyderabad
Bachelor of Technology · Computer Science & Engineering
N/A – June 30, 2021
Amazon Development Centre
Business Intelligence Engineer (BIE) (Investigation Specialist)
January 1, 2021 – Present
Hyderābād, Telangana, India
Defect Pattern Analysis - Investigation Quality (Ongoing)
June 17, 2026 – June 1, 2026
Ran recurring root cause analysis cycles on team audit data to identify the top defect categories driving quality dips. Built a structured coaching programme targeting those patterns, tracking improvement across two quarters. Findings presented to leadership and referenced in process documentation.
Case Volume & Quality Dashboard
June 17, 2026 – June 1, 2026
Designed a Power BI dashboard to track weekly investigation volume, quality scores, SLA adherence, and defect trends across a team of 800. Replaced a manual copy-paste reporting process. Dashboard adopted as the standard weekly operations review format – still in active use.
Backlog Analysis & Triage - 30,000 Case Spike
June 17, 2026 – Present
When the investigation volume jumped unexpectedly from ~300 to 30,000 cases, I analysed the case composition to segment by type, complexity, and resolution priority. Used that analysis to design a triage framework, reallocate team capacity, and set SLA expectations for each tier. Backlog reduced from 30,000 to 7,000 within two weeks while quality scores held.
Seller Segmentation - K-Means Clustering
June 17, 2026 – Present
Applied K-Means clustering to a synthetic e-commerce seller dataset built from patterns observed in Amazon FBA investigations. Used the elbow method and silhouette scores to determine optimal K=3. Identified three seller cohorts with materially different risk profiles – low-volume legitimate sellers, high-volume borderline accounts, and a small high-risk cluster driving 60%+ of reimbursement anomalies. Visualised cluster separation using PCA-reduced 2D plots.
Google Project Management: Foundations
Coursera
June 1, 2026 – Present
Protecting Against Money Laundering & Terrorist Financing
Coursera
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from operational dashboards to advanced machine learning applications, indicates a broad interest and adaptability. Their experience at Amazon, a large tech company, suggests familiarity with structured processes and a performance-driven culture. The focus on fraud detection and operational efficiency aligns well with roles requiring meticulous attention to detail and impact. The use of AI tools like ChatGPT and Claude in their workflow also shows a proactive approach to leveraging new technologies.
Soft Skills & Operational Fit
The candidate demonstrates strong analytical thinking, problem-solving capabilities, and a results-oriented approach, as evidenced by their project descriptions (e.g., reducing a 30,000-case backlog, identifying defect patterns). Their experience at Amazon suggests an ability to operate in a fast-paced, data-intensive environment. The descriptions also highlight leadership potential in driving process improvements and presenting findings to leadership.