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Kamal Ede
Mid-level Data Engineer specializing in cloud data platforms, Spark, and streaming pipelines
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About
Data/MLOps engineer (Cognizant background) who owned an AWS/Airflow/Snowflake healthcare transactions pipeline processing ~8–10M records/day and cut pipeline/data-quality incidents by ~33%. Also built and deployed a production FastAPI model-inference service on Kubernetes (Docker, HPA) with strong observability (Prometheus/Grafana), versioned endpoints, and resilient backfill/idempotent external data ingestion patterns.
Experience
Education
Key Strengths
- Owned end-to-end healthcare data pipeline into Snowflake (8–10M records/day) on AWS with Airflow
- Built robust ingestion from REST APIs and source DBs with pagination + incremental loads
- Implemented data validation/quality checks (counts, schema compliance, anomaly detection) with logging/alerting
- Reduced data quality and pipeline failure incidents by ~33%
- Designed resilient external data collection with throttling, idempotent loads, and parameterized backfills
- Shipped low-latency (<100ms) model inference service using FastAPI, Docker, and Kubernetes with autoscaling
- Established API versioning (/v1), strong input validation/error handling, and developer-friendly OpenAPI docs
- Greenfield delivery: scoped MVP, set up CI/testing, staging rollout, and observability with Prometheus/Grafana
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