Karishma Kaiz

<p>Spearheaded requirement analysis, source-to-target mapping, and data validation for workflow mining initiatives, collaborating with business analysts and stakeholders to ascertain application workflows, reporting needs, and underlying source systems.<br>
Architected metadata-driven ETL pipelines in Azure Databricks (PySpark, Spark SQL) utilizing a 3-layer Medallion Architecture (Bronze → Silver → Gold), facilitating the onboarding of new data sources without requiring pipeline modifications.<br>
Developed reusable, parameterized Databricks notebooks for dynamic data ingestion, merging, transformation, cleansing, normalization, and aggregation according to business rules, thereby reducing redundant development efforts.<br>
Automated recurring manual data engineering tasks using Python/PySpark scripts, enhancing daily operational productivity and earning Orion's SPOT Out-of-the-Box Thinking Award.<br>
Remedied high-priority production defects with zero reoccurrences, directly improving data quality, pipeline stability, and processing reliability for downstream Power BI reporting.<br>
Chaired a Microsoft Fabric modernization Proof of Concept (PoC), migrating data from Azure IaaS/PaaS to Fabric capacity and OneLake, which included SQL Server database restoration and the consolidation of multiple databases into a unified Bronze layer; findings significantly influenced the organization's Fabric adoption strategy.<br>
Delivered structured, governed datasets (Delta Lake, Serverless Compute, Unity Catalog) to the Power BI team, empowering data-driven decision-making across business reporting functions.<br>
Conducted knowledge-transfer sessions on Databricks and migration strategies, mentoring new team members and documenting PoC scope and limitations for organizational reuse.</p>

<p>Engineered and optimized enterprise ETL pipelines utilizing Azure Databricks, Azure Data Factory (ADF), Python, PySpark, and SQL, supporting large-scale data integration and reporting for enterprise clients for over five years.<br>
Orchestrated the migration of legacy Informatica ETL workflows to Azure Databricks as a critical component of the client's cloud modernization program.<br>
Implemented Slowly Changing Dimensions (SCD), Master Data Management (MDM), and data validation frameworks, enhancing data quality and consistency within dimensional models and data warehouse solutions.<br>
Established and maintained CI/CD pipelines with Azure DevOps and Git, enabling automated deployment, version control, and streamlined release management.<br>
Fine-tuned Spark jobs, SQL queries, and Databricks workloads, decreasing execution times and improving processing performance across production pipelines.<br>
Monitored batch workflows and pipeline executions, proactively troubleshooting failures to ensure Service Level Agreement (SLA) compliance, receiving commendations twice from project leadership for timely and defect-free delivery.<br>
Performed code reviews, enforced coding standards, and authored comprehensive technical documentation, architecture diagrams, and operational runbooks.<br>
Mentored junior engineers and delivered knowledge-transfer sessions on Azure Databricks, ADF, and cloud migration strategies while supporting Agile/Scrum delivery using Jira and Confluence.</p>