Diagnosed cases inconsistency in data architecture which reported, corrected to architects favoring data process 100%. Successful design connections Databricks and Data Factory platform, ensuring by 80% protection of sensitive auth data.

Implemented efficient ETL with solid practices in Dataflow Databricks API Pyspark and Scala, minimizing 80% in cluster consumption and reduced develop time.

Reference Data Engineer in the Squad where lead and train team in data ingestion, such as data dump SQL Server and Teradata sources to ADLS Gen2 and implement robust data strategy in Data Factory.

Migrate data services ETL developed in Hadoop Cloudera to modernized Databricks platform, ensuring data integrity in Hbase and ADLS Gen2, minimizing downtime by 95%.

Implemented automated testing QA AWS, leading to a 100% improvement in testing efficiency reducing manual efforts. Identify KPIs and develop custom dashboards in Redash.

Design and proposed a scalable, efficient and reliable data ecosystem, pipeline processing AWS Glue, automation and provisioning cloud resources Lake Formation, increasing governance, security and centralization data on AWS S3.