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Spandana Bellamkonda
Mid-level Data Analyst specializing in financial and telecom analytics
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Analytics candidate with hands-on experience at AT&T building SQL/Python pipelines for churn, usage, billing, and network-performance data at multi-million-row scale. Stands out for combining strong data quality and reconciliation practices with measurable operational impact, including a 30% query runtime improvement and ~8 hours/week of reporting automation savings.
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Key Strengths
- Built SQL pipelines for multi-million-row telecom datasets
- Strong data quality remediation across nulls, duplicates, and mismatched keys
- Reconciliation-driven validation aligning revenue to finance reports
- SQL performance optimization with 30% runtime reduction
- Automated Python analytics workflows using pandas and SQLAlchemy
- Reduced manual reporting effort by about 8 hours per week
- End-to-end ownership of churn analytics from metric definition to dashboard delivery
- Stakeholder-aligned KPI design and dashboard adoption
- Operationalized churn metric with edge-case handling for seasonal inactivity and new users
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