I’m a Data Engineer with 3+ years of experience building and optimizing large-scale ETL pipelines in enterprise production environments. I specialize in designing reliable, high-performance data workflows that process multi-terabyte datasets with a strong focus on scalability, automation, and data quality.
- Design and optimize large-scale ETL pipelines
- Work with distributed data processing frameworks like PySpark
- Build cloud-based data solutions using Google Cloud Platform (BigQuery, GCS, Dataflow)
- Automate workflows using Python and Shell scripting
- Improve performance through query optimization and partition strategy
- Ensure production stability through monitoring, root cause analysis, and SLA-driven engineering
- Python
- SQL
- Bash
- PySpark
- Apache Spark
- Ab Initio
- Google Cloud Platform (BigQuery, Cloud Storage, Dataflow)
- Teradata
- Control-M
- Unix/Linux
- Data Warehousing
- ETL/ELT
- Data Modeling
- Performance Tuning
- Distributed data processing projects
- Spark optimization experiments
- Scalable batch pipeline designs
- Cloud-native data architecture explorations
- System design practice for data engineering
Currently working in enterprise data environments, managing 200+ production ETL jobs processing 10TB+ data daily. My work involves pipeline optimization, automation, incident management, and cloud migration initiatives.
I’m actively transitioning toward modern, cloud-native data engineering with a strong focus on distributed systems and scalable architecture.
- Advanced PySpark optimization
- Distributed systems fundamentals
- Modern orchestration patterns
- Data engineering system design
I’m always open to collaboration, knowledge-sharing, and discussions around data engineering, cloud architecture, and scalable data systems.