Global Data Analyst.Employment

The data infrastructure ran on legacy batch processes that couldn’t keep up with growing operational and analytical needs across countries.

Led the cloud migration of the entire data infrastructure to GCP, designing a BigQuery-based data warehouse with 50+ scheduled ETL pipelines and migrating 10+ TB within a quarter.

Operations monitoring relied on a manual recurring job to spot business scenarios across markets, taking up to 2 days of analyst work per cycle and creating detection lag.

Automated the operational monitoring workflow within the first 6 months of the role. Python jobs querying PostgreSQL surfaced supply gaps, driver behaviour anomalies, fraudulent trip patterns, and other scenarios into Google Sheets for the operations team, replacing a 2-day manual job with a 5-15 minute automated cycle.

The agent and partner incentive payment process took multiple days, with manual reviews creating delays, errors, and a heavy load on the finance team.

Built automated data pipelines for incentive payments combining predictive analytics and near-real-time monitoring. Reduced a multi-day manual process to minutes, with structured logs and dispute trails for finance and support.

Financial defaults from undetected fraud patterns were eroding margins, with no central knowledge base of known schemes or systematic daily collection monitoring.

Implemented a fraud patterns database fed by automated daily collection monitoring, surfacing anomalies the same day they appeared. Achieved zero unknown financial defaults during the period and laid the groundwork for the later ML-based fraud screening pipeline.

Marketing campaigns were broadly targeted because the team lacked a structured user profiling layer, leading to low engagement and inefficient spend.

Built a profiling database covering riders and drivers, with RFM segmentation (recency, frequency, monetary) and cohort analysis used by Marketing for targeted campaigns. Translated cohort behavior into operational signals across retention, monetization, and re-engagement, supporting rider retention and growth.

Country managers and top management lacked a single daily lens on business health across markets, with no consolidated KPI rhythm or recurring insight delivery.

Built business health dashboards in Google Sheets, transitioning to Google Data Studio and Tableau after the BigQuery migration. Consolidated 20+ KPIs into a daily report spanning all business areas. Presented insights daily to country managers and top management, surfacing trends and anomalies for operational decisions.