data engineering , Data Warehousing, Databricks, Data Modeling, Azure, AWS, LLMs, enterprise data architecture, Microsoft Fabric, AI architecture

data engineering , Pyspark, Spark, Data Governance, Python, Sql, Generative AI, oci, Delta Lake, Oracle AI Data Platform, AIDP

Power Bi, Azure Databricks, Data Warehousing, Sql, Etl, ELT, Microsoft Fabric

Data Modeling, Sql, Python, Performance Tuning, Machine Learning, Data Architecture, Analytics

snowflake , BigQuery, Redshift, Sql, Azure Data Factory, Spark, Databricks, Python, AWS, Airflow, Synapse, dbt, Delta Lake

Sql, Databricks, Pig, Pl Sql, Impala, RDBMS, Kinesis, Hadoop, Pyspark, AWS, Etl, Cassandra, Hive, Unix Shell Scripting, Python, Azure, Neo4j, Terraform, Apache Kafka, Git, MongoDB, Spark, CircleCI

Azure Functions, SQL Server, Azure DevOps, GitHub Actions, Microsoft Entra ID, T-SQL performance tuning, AlwaysOn Availability Groups, Service Bus, Azure App Services

, Yarn, Java, Presto, Google Cloud, Kafka, AWS, Redis, Node.JS, Hive, Cassandra, Python, Azure, Golang, MongoDB, Spark, Airflow, HDFS, Map-Reduce, NoSQL databases, TiDB

data engineering , Metadata Management, Data Architecture, Edw, Data Lake, Ml, Data Quality, Data Governance, Databricks, Data Integration, data pipelines, data fabric, cloud-native data platforms, GenAI data readiness, OLTP to lakehouse architecture, modern data architectures, enterprise integration patterns, unified data access layer, Hadoop-based platforms, enterprise data architecture, Lakehouse architectures, data platform architecture, Ai, Teradata, architecture roadmap, Transformation

snowflake , Sql, Tensorflow, Pandas, Pytorch, Python, Spark, RAG pipelines, Agentic AI systems, MLflow, Kubeflow, Scikit-learn, vector databases, MCP Model Context Protocol, Generative AI LLM Proficiency, MLOps practices, prompt engineering, LLM-powered applications