Japjot Singh

Senior Data Engineer @J.P. Morgan

NY, US

Architected fault-tolerant ETL pipelines using Python, SQL, and Spark, reducing data ingestion failures by 40% and improving system resilience-Designed AWS Glue workflows integrated with Step Functions and Lambda to orchestrate batch and real-time data flows with 99% accuracy and automated error handling-Automated provisioning of EC2, Redshift, and RDS using Terraform, Boto3, and Lambda triggers, cutting manual infrastructure setup by 80% and enabling event-driven orchestration-Enforced IAM-based access controls and enabled KMS encryption on S3, Redshift, and RDS, securing sensitive data at rest and in transit, and ensuring HIPAA/SOC 2 compliance across ETL workflows-Tuned Snowflake queries with clustering keys and micro-partition pruning, reducing Power BI dashboard load times by 35%-Built DBT models with SCD2 logic and materializations to support historical tracking and downstream analytics in Snowflake-Developed custom Airflow DAGs for orchestrating multi-stage ETL jobs, reducing data validation time from 5 hours to 30 minutes-Proficient in writing complex SQL queries on large structured and semi-structured datasets, leveraging Python automation to streamline data processing workflows, reduce manual intervention, and accelerate insight delivery-Implemented robust data governance practices using DBT tests, schema validation, and Snowflake lineage tracking for audit readiness-Standardized data quality checks for business-critical pipelines, enabling anomaly detection and proactive alerting on pipeline issues-Collaborated with BI teams to deliver real-time insights through Tableau dashboards powered by fresh Snowflake data-Integrated CI/CD workflows using GitHub Actions to deploy DBT models and Airflow DAGs, improving deployment consistency-Ensured compliance with internal data policies and security protocols by leveraging Snowflake’s RBAC and masking policies.

Adelphi University

Master's degree