Aleakhya Pandeti

• Reduced manual data processing time by 50% by engineering automated ETL pipelines in Python and Apache Airflow, cutting
reporting turnaround from days to hours across 6 GCC markets.
• Improved data reliability across 5+ source systems by designing validation and standardisation workflows, reducing data
inconsistency errors by an estimated 70% and ensuring 100% accuracy across all downstream reporting.
• Scaled data output capacity by 3x by consolidating fragmented datasets from 5+ disparate sources into unified, clean pipelines,
enabling repeatable data delivery for cross-functional commercial teams.
• Eliminated 4+ recurring reporting bottlenecks by diagnosing data quality gaps and implementing targeted process
improvements, saving an estimated 10+ hours of manual rework per week.

• Replaced 70% of manual tracking processes by building an end-to-end data platform in Dash and Snowflake SQL, reducing
administrative overhead by 40% and eliminating spreadsheet-based workflows.
• Improved real-time operational visibility for 4 distinct stakeholder groups by designing role-based interactive dashboards,
reducing manual status reporting by an estimated 30%.
• Cut manual follow-ups by 60% and reduced data delivery lag by 5+ hours per week by automating notification and reporting
workflows using interval and fixed-time scheduling.
• Reduced structured report generation from a 2-3 day manual process to on-demand delivery by building automated Excel and
PDF output pipelines directly from live backend data.