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.
