Pros
● Designed cloud-based data pipelines to process 2M+ monthly trip, pricing, and operational records, applying metadata-driven ingestion and performance tuning to reduce ETL runtimes by 35% and improve data freshness for analytics teams. ● Automated cross-functional analytics workflows using Python (Pandas) and SQL, building reusable transformation layers that eliminated manual data consolidation and saved 150+ engineering hours annually. ● Monitored data ingestion services, SQL warehouses, and REST-based event streams, implementing alerting and logging that maintained 99.5% pipeline availability across 120+ workflows and minimized latency and data-drift incidents. ● Developed Power BI dashboards sourcing from cloud data lakes and SQL layers, delivering 30+ mobility and marketplace KPIs (supplydemand balance, utilization, SLA adherence) and reducing reporting delays by 3 business days. ● Implemented role-based access controls (RBAC) and audit logging across shared analytics datasets for 200+ users, strengthening data security while enabling safe self-service access for product and operations teams. ● Standardized Git-based CI/CD workflows with automated validation and controlled deployments, preventing 12+ production rollbacks and improving reliability of data platform releases.
Cons
Their is No Cons for me for this company.
