Data Quality (DQ) rules, Metadata Management, Unix, Databricks, Python, Data lineage techniques, Master data management, Data modeling methodologies, Big Data storage architecture

Workflows, Apis, Cloudformation, Pyspark, Sql, Devops, MLops, Terraform, Spark, Databricks, Azure, Python, AWS, Databricks Lakehouse, dbt

Azure Data Factory, Pyspark, Databricks, Sql, Azure DevOps, CI CD workflows, ADLS Gen2, Event Hub, Azure-native technologies, Azure SQL Database

Spark SQL, Pyspark, Apache Spark, Sql, Git, Hive, Linux, Hiveql, Data Modelling, Python, Hadoop Hdfs, Apache Iceberg, Parquet, Distributed-system troubleshooting, Spark performance tuning, Source-to-target mapping, Hive Metastore, Large-volume ETL data migration, Data validation reconciliation

BigQuery, Scala, Apache Spark, Kafka, Dataproc, Jenkins, Git, Hive, Gcp, Etl, workflow orchestration tools, CI CD, Pub Sub, GCS

snowflake , BigQuery, AWS Glue, Redshift, Sql, Data Integration, Azure Sql, ELT, Azure Data Factory, Gcp, Data Architecture, Databricks, Azure, Python, AWS, Etl, Airflow, Cloud Data Platforms

snowflake , Java, Scala, Pyspark, Apache Spark, Sql, Gcp, Terraform, Docker, Databricks, Azure, Python, Kubernetes, AWS, Airflow, Deequ, Great Expectations, Prefect

Java, Scala, Restful Apis, Kubernetes, Python, Sql, OLAP and OLTP systems, big data engineering

Java, BigQuery, Hadoop, Data Modeling, Kafka, ELT, Microservices, Data Quality, Hive, MLops, Spark, Databricks, Data Governance, Python, Kubernetes, Etl, Airflow, HDFS, Delta Lake, CI/CD