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Kavita Tamire
Mid-level Data Engineer specializing in AWS cloud data platforms
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About
Data engineer with Charter Communications experience modernizing large-scale AWS data lake pipelines: ingesting S3 data, validating against legacy systems, transforming with PySpark/Spark SQL, and serving via Iceberg/Delta tables. Worked at 50M–300M record scale, delivered >99.5% data match, and built monitoring/alerting (CloudWatch/SNS) plus retry orchestration (Step Functions) and data quality gates (Great Expectations).
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Key Strengths
- Built and deployed end-to-end AWS data lake pipelines (ingestion to serving) using Spark/PySpark and Iceberg
- Handled large-scale datasets (50M-record sources; ~300M records used in complex transformations)
- Achieved >99.5% data match vs legacy systems through stepwise validation and QA re-validation
- Designed for schema evolution using Iceberg/Delta tables (schema changes, ACID, time travel)
- Implemented pipeline observability and alerting with CloudWatch + SNS
- Implemented orchestration retries/failure handling using AWS Step Functions
- Established data quality gates with Great Expectations and confidence-based routing (steward review vs reject)
- Applied SQL performance optimization techniques (EXPLAIN ANALYZE, avoiding SELECT *)
- Implemented data access controls via row-level security
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