Saif Khan

• Monitored Databricks and Delta Lake pipelines within a Lakehouse architecture, proactively identifying and resolving failures to
maintain >99% pipeline availability.
• Supported migration of Databricks workloads from Hive Metastore to Unity Catalog, improving data governance, access control,
and centralized metadata management across the data lakehouse platform.
• Leveraged GitHub-based CI/CD pipelines to deploy Databricks workflows from development to production, reducing deployment
errors by 30%.
• Conducted root cause analysis for reporting discrepancies by tracing ETL transformations and resolving column-level mismatches,
improving reporting accuracy.
• Collaborated with cross-functional teams (business, analytics, QA) to deliver scalable data solutions and improve operational
efficiency.

• Designed and maintained scalable ETL pipelines and workflow orchestration using Azure Data Factory, enabling reliable data
ingestion into Azure Data Lake Storage (ADLS Gen2).
• Implemented data quality and cleansing frameworks (null handling, duplicates, constraint enforcement), improving data accuracy.
• Executed data migration projects consolidating heterogeneous sources into ADLS, reducing storage costs by 20% and improving
access times by 25%.
• Implemented basic and advanced Change Data Capture (CDC) using Databricks CDF, enabling incremental data processing and
reducing compute costs by 30%.
• Optimized Databricks SQL and Spark workloads on Delta Lake tables using partitioning, indexing, and query performance
optimization techniques, achieving 40% faster execution.