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Parvinder Singh
Mid-level Data Engineer specializing in AWS lakehouse platforms and scalable ETL/ELT
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Data engineer focused on reliable, production-grade pipelines and data services: has owned end-to-end ingestion-to-serving workflows processing millions of records/day, using Airflow, Python/SQL, and PySpark. Demonstrates strong operational rigor (monitoring, retries, idempotency, backfills) and measurable outcomes (98% stability, ~30% faster processing), plus experience exposing curated warehouse data via versioned REST APIs.
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Key Strengths
- Owned end-to-end data pipelines processing millions of records daily
- Improved pipeline stability to 98%
- Reduced processing time by ~30%
- Strong data quality engineering (schema validation, deduplication, null checks) in Python/SQL
- Handled late-arriving and duplicate data using watermarking and SQL window-function deduplication
- Designed idempotent pipelines and safe backfill mechanisms to prevent duplicates
- Built monitored, retryable ingestion workflows in Airflow (including exponential backoff)
- Shipped REST data services with performance optimizations (caching, pagination, query tuning) and API versioning
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