Data Analyst with 5+ years in Qlik Sense, SQL & Fraud Analytics
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Data Analyst with experience in SQL, Qlik Sense, QlikView, and business intelligence reporting. Skilled in dashboard development, data analysis, KPI monitoring, data visualisation, ETL processes, and fraud analytics. Experienced in transforming raw data into actionable insights using SQL queries, data modelling, and interactive reporting solutions to support business decision-making.
Magadh University
Master of Arts · Economics
N/A – June 30, 2019
Gaya College
Master of Arts · Economics
N/A – June 30, 2019
ApnaTime Tech
Data Analyst
July 1, 2024 – Present
Bengaluru, Karnataka, India
ApnaTime Tech
Senior Operation Executive
April 1, 2021 – June 1, 2024
Bengaluru, Karnataka, India
Fraud Detection & Risk Scoring System
June 22, 2026 – Present
Developed SQL-based fraud detection logic to identify suspicious recruiters using payment patterns, OTP misuse, and complaint frequency analysis. Built interactive Qlik Sense dashboards for monitoring fraud trends, high-risk recruiters, and operational alerts. Reduced manual investigation efforts by automating fraud pattern identification and improving operational visibility.
Candidate & Recruiter Behaviour Analytics Dashboard
June 22, 2026 – Present
Built Qlik Sense dashboards with KPIs like: Job postings per recruiter Conversion rate (job posted → candidate applied → hired) Complaint rate per recruiter Highlighted abnormal patterns such as: Recruiters with high job postings but low hiring Sudden spike in candidate complaints Created filters for location, industry, and recruiter ID Impact: Enabled the ops team to quickly identify suspicious recruiters Improved decision-making with real-time insights Reduced manual analysis effort
Payment Fraud & Complaint Analysis System
June 22, 2026 – Present
Queried complaint data using SQL to categorise fraud types: Registration fee scams Fake job offers Salary fraud Performed trend analysis (daily/weekly fraud spikes) Built Qlik reports showing: Top fraud categories High-risk recruiters Region-wise fraud distribution Correlated payment data + complaint data to detect fraud patterns. Impact: Identified top fraud patterns affecting users Helped improve user safety policies Supported internal teams in faster case resolution
Cultural Fit Analysis
The candidate's project diversity, focusing on fraud detection, risk scoring, and behavioral analytics, aligns well with roles requiring analytical problem-solving and impact-driven insights. Their experience in a startup environment (ApnaTime Tech) suggests adaptability and a proactive approach, which are positive indicators for cultural fit in dynamic organizations. The breadth of skills in SQL, Qlik Sense, and data analytics tools demonstrates a versatile profile suitable for various data-centric teams.
Soft Skills & Operational Fit
The candidate's experience as a Senior Operation Executive, including leading quality assurance, conducting fraud investigations, and mentoring junior team members, indicates strong operational acumen, attention to detail, and leadership potential. Their ability to document procedures and audit processes suggests a methodical and accountable approach to work. These skills are highly valuable for a senior data analyst role, especially in understanding business context and driving data-driven operational improvements.