Java, Rust, Hadoop, Pyspark, Apache Spark, Sql, Gcp, Data Governance, Azure, Python, AWS, R, ETL architectures, data security best practices

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

Orchestration, S3, Metadata Management, Pyspark, PostgreSQL, Automated Testing, Data Modeling, Lambda, Advanced Sql, Python, RDS, release management, Dynamodb, SQL Server, Redshift, Data Quality, ECS, transformation standards, CI CD, AWS data services, ETL best practices, data cataloging, Step Functions, Monitoring, large-scale data processing, Glue

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

Java, BigQuery, Dataproc, Sql, ELT, Devops, Cloud Storage, Compute Engine, Spark, Apache Beam, DataFlow, Data Analytics, Kubernetes, Python, Etl, Air Flow, Colab, cloud security best practices, Looker, Data Lake Engineering, Pub Sub, Vertex AI

Github, Data Modeling, Data Warehousing Concepts, Sql, Git, Azure Data Factory, Python, Aws S3, Airflow, CI CD Pipelines, Snowflake Data Cloud, DBT Data Build Tool

microsoft azure devops, Data quality assurance and monitoring techniques, Cloud-based data storage and processing solutions, Data pipelines