data engineering , Data Governance, Data integration techniques and tools, Data Analysis Interpretation, Data Architecture Principles, Cloud-based data platforms, Distributed computing environments

data engineering , Data Factory, Query Optimization, Data Modeling, Pyspark, Performance Tuning, Data Migration, Data Architecture, Data Governance, Data Quality, Data Ingestion, OneLake, Spark Notebooks, Troubleshooting, Reconciliation, Azure Event Hubs, Eventhouse, Azure IoT Hub, Monitoring, Transformation Pipelines, Validation, KQL, Fabric Real-Time Intelligence, KQL DB, Eventstreams, Lakehouse, Microsoft Fabric, Production Support

Automation Tools, Data Extraction, Performance Monitoring, Coding Standards, Security remediation, Generative AI tools, Troubleshooting

Pyspark, Sql, Databricks, Python, Data Transformation, Data Warehousing, Data Modeling, ELT, Data Quality, Data Governance, Data Integration, Etl, AI-powered analytics, Compliance, pipeline orchestration, Security, Lakehouse architecture

snowflake , S3, Pyspark, Data Modeling, Sql, Big Data Technologies, Sns, automation, Python, AWS, Etl, Data Processing, Glue, AI-native development practices, performance optimization

BigQuery, Pyspark, Dataproc, Data Modeling, Sql, ELT, Git, Python, Etl, GCP data services, Cloud Dataflow, Pub Sub, Cloud Composer, CI CD tools, performance optimization, partitioning, GCS