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Nikitha Margadi
Mid-level Data Engineer specializing in cloud lakehouse, streaming, and MLOps
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Data engineer at AT&T focused on large-scale telecom (5G/IoT) data platforms, owning end-to-end pipelines from Kafka/Azure ingestion through Databricks/Delta Lake transformations to serving analytics and ML. Has operated at very high volumes (~50+ TB/day) and delivered measurable performance gains (25–30% faster processing) plus improved reliability via Airflow monitoring, robust data quality checks, and resilient external data collection patterns (rate limiting, retries, dynamic schemas).
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Key Strengths
- Owned end-to-end telecom data pipelines (ingestion to serving) at ~50+ TB/day scale
- Improved pipeline processing time ~25–30% via partitioning and Delta Lake optimizations
- Reduced duplicate/null-related data issues through Python-based data quality checks and schema validation
- Built monitoring/alerting with Airflow to catch failures early and reduce downstream impact
- Designed resilient external data collection with retries, checkpoints, batching, and exponential backoff for rate limits
- Implemented API versioning/backward compatibility and documentation for internal data services
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