Pyspark, Apache Spark, Sql, Rabbitmq, Rdf, Neo4j, Apache Kafka, Python, Airflow, GraphDB, owl, pgvector, Stardog, Great Expectations, Pinecone, Flink, AWS Glue DataBrew, Chroma, dbt, Dagster, Milvus, Weaviate, Apache Jena

T-sql, SQL Server, Azure Data Factory, Databricks, Python, Etl, Logic App, Azure Data Lake Storage Gen2, Event Hub, Azure Monitoring, Azure Suite Fabric, Microsoft Purview, Stream Analytics, Data Explorer, CosmosDB

Database Design, Data Governance, Data Architecture Principles, Integration methods, Security principles, Data storage solutions, Data modeling techniques

Github, Power Bi, Pyspark, Datadog, Sql, Jenkins, Git, Azure Data Factory, Distributed Systems, Gitlab, Splunk, Python, cloud-based microservices, Batch Systems, Data Bricks, Synapse, CI CD pipeline, Dedicated SQL Pool, ADLS, Azure services, Spark Pool, cloud-native architectures

Scala, Pyspark, Apache Spark, JIRA, Sql, Jenkins, Git, Confluence, Powerbi, Terraform, Gitlab, GCP Data Lake, MLflow, GCP Pub Sub, GCP Cloud Storage, Vector Databases, Delta Lake, Looker Studio

snowflake , Sql, Databricks, SQL Server, automation, Data Modeling, Python Scripting, Oracle, Control M, Git, Airflow, dbt, CI CD, DevOps practices, cloud data warehouses

Azure, AWS, Data Modeling, Data Integration, Data Governance

Databases, Agile Methodologies, Data Warehousing, Sql, ELT, Python, Etl, GenAI, knowledge graphs, data analytics tools, vector databases, LLM-enabled solutions, Data Storage, retrieval architectures, AI application frameworks, cloud data platforms