Big Data
- Design, implement, and manage large-scale data systems using distributed architectures like Hadoop and Spark to support high-volume data processing and analytics.
Python
- Use Python for data ingestion, transformation, modeling, scripting pipelines, and automation—leveraging popular libraries such as Pandas, PySpark, and more for efficient data workflows.
ADB (Azure Databricks)
- Unified analytics platform built atop Apache Spark optimized for the Azure ecosystem—supporting scalable data engineering pipelines, interactive notebooks, collaborative development, and efficient data processing in the cloud.
Snowflake
- Cloud-native data warehousing solution offering scalable storage, high-performance querying, real-time data processing, seamless integration, and centralized data insights.
ETL (Extract, Transform, Load)
- Complete lifecycle of data processing: retrieving data from various sources, transforming it through cleansing and aggregation, and loading it into data lakes, warehouses, or target systems for analytics and reporting.
