Experience

Designed and built scalable big-data pipelines for high-volume ingestion, transformation and validation, supporting enterprise risk mitigation and data migration. Kept data quality, fault tolerance and SLA compliance across batch and real-time workloads.

records per day

10M+

data quality

99%

latency reduction

40%

• Built batch and streaming ETL with PySpark, Kafka and Airflow, with DAG-level monitoring and retry logic.
• Built medallion-architecture lakes on AWS S3, with Snowflake transformation layers for analytics-ready data.
• Contributed to production deployments, CI/CD automation and operational monitoring, applying MLOps and AIOps practices.
• Improved pipeline reliability and downstream observability with CloudWatch and Grafana dashboards.

• Python
• PySpark
• Kafka
• Snowflake
• Airflow
• AWS S3
• Docker
• MLOps

Coursework in machine learning, deep learning, distributed systems and large-scale data engineering, alongside research-grade projects and applied AI work.

• Machine learning
• Distributed systems
• Data structures
• Statistics
• DBMS

A foundation in mathematics, physics, chemistry and computer science, and the start of an interest in programming and applied problem-solving.

• Mathematics
• Physics
• Computer Science