Nafeezuddin Mohammed

About

Data engineer with experience at Sony and Walmart building high-volume, near-real-time analytics and ingestion systems. Has owned end-to-end pipelines from Kafka/Spark streaming through S3/Parquet and Redshift/Looker, emphasizing data quality (Great Expectations), observability (CloudWatch/Azure Monitor), and reliability (Airflow SLAs, retries, checkpointing), including measurable performance and latency improvements.

Key Strengths

• Owned end-to-end streaming analytics pipeline (Kafka → Spark → S3/Parquet → Redshift → Looker) at tens of millions of events/day
• Improved pipeline performance ~40% and reduced latency to near real-time
• Implemented robust data quality validation with Great Expectations (schema/null/range checks) plus monitoring/alerting
• Handled schema drift and upstream inconsistencies with defensive PySpark logic and schema evolution practices
• Built dimensional models in Redshift (fact/dimension tables) to support business reporting
• Designed resilient orchestration with Airflow (SLA monitoring, retries, checkpointing) and backfill utilities
• Built large-scale external/vendor data ingestion at Walmart via Azure Data Factory (hundreds of millions of records weekly) with monitoring, retries, and gated curated layers
• Stood up an ambiguous early-stage near-real-time metrics system end-to-end including CI/CD (GitHub Actions) and IaC (Terraform)

Experience

Education

Awards

• Recognized for optimizing large-scale data platforms at Sony

Skills

Languages

Certifications