AMAN KUMAR JHA

Data Engineer

New Delhi, India $25/hr Remote Full-time 1+ yrs exp 88 · Excellent

About

Data Engineer with 1.5+ years of production experience building and owning high-throughput ETL and real-time streaming pipelines for a global investment management firm (T. Rowe Price) via Tata Consultancy Services. Delivered a 35% reduction in PySpark pipeline runtime, 99.5% Airflow pipeline uptime, and 25% faster Snowflake query execution across financial data workflows processing over 1 million records daily. Proven ability to translate business requirements into reliable, scalable data infrastructure in regulated, enterprise-grade environments.

Skills & Expertise (17)

SQL Intermediate

7.5/10

1.5

Years Exp

Apache Airflow Intermediate

7.5/10

1.5

Years Exp

ETL Pipelines Intermediate

7.5/10

1.5

Years Exp

Python Intermediate

7.5/10

1.5

Years Exp

Apache Spark Intermediate

7.0/10

1.5

Years Exp

PySpark Intermediate

7.0/10

1.5

Years Exp

Snowflake Intermediate

7.0/10

1.5

Years Exp

Git Intermediate

7.0/10

1.5

Years Exp

Data Modeling Intermediate

6.5/10

1.5

Years Exp

Data Warehousing Intermediate

6.5/10

1.5

Years Exp

Data Quality Intermediate

6.5/10

1.5

Years Exp

Apache Kafka Intermediate

6.0/10

1.5

Years Exp

Delta Lake Intermediate

6.0/10

1.5

Years Exp

GitHub Actions Intermediate

6.0/10

1.5

Years Exp

Data Lakes Intermediate

6.0/10

1.5

Years Exp

Kubernetes Programming Languages: Python

Work Experience

Data Engineer

Tata Consultancy Services (TCS)

Aug 2024 - Mar 2026

Worked within the embedded data engineering team for T. Rowe Price to build scalable batch and streaming pipelines supporting financial analytics and reporting workflows. Redesigned and optimized PySpark ETL pipelines processing 1M+ financial records daily — revised end-to-end runtime by 35% through adaptive partitioning, broadcast joins and Spark execution optimization. Engineered 15+ production Apache Airflow DAGs for financial data ingestion, transformation, and reconciliation workflows using custom operators and sensors — raised pipeline uptime from ~92% to 99.5%, reducing on-call escalations by 40% and eliminating daily manual interventions. Architected Snowflake data models (star schema, aggregation layers) and rewrote high-cost analytical SQL queries — reduced average BI query latency by 25%, enabling faster report generation for portfolio risk and compliance teams. Deployed and managed Spark and Airflow workloads on AWS EKS (Kubernetes) using containerized infrastructure — enabled horizontal autoscaling and standardized deployment across 3 production environments (dev, staging, prod). Implemented schema validation, reconciliation checks, SLA alerts, and data quality monitoring across ETL workflows, reducing downstream BI data incidents by ~60%.