Delivered end-to-end ETL migration work (discovery through production), supporting migration from Azure-based notebooks to Google Cloud Platform (GCP). Converted notebook-driven analytical logic into production-ready pipelines; designed and implemented Apache Airflow DAGs under a proprietary client framework. Operationalized workflows in Astronomer to ensure reliable scheduling, version control practices, and operational stability. Collaborated with data and architecture teams to enforce data governance, quality standards, and ETL best practices across cloud-native environments. Improved Power BI rate reporting by refining DAX measures and aligning dataset outputs with reporting definitions (rates/KPIs), reducing inconsistencies and improving stakeholder confidence.

Led migration and optimization of enterprise pipelines from legacy systems (SQL Server, Informatica) to Databricks on Azure, reducing runtime and maintenance overhead. Developed modular Python components for extraction, transformation, and validation aligned with internal governance standards. Built automation scripts supporting authentication layers and real-time validations for reporting pipelines, aligned with audit requirements. Contributed to CI/CD workflows using Azure DevOps, improving development cycles and integration of internal analytics tooling. Built and optimized PySpark and SQL transformations in Databricks on Azure to support analytics-ready datasets and downstream reporting.

Designed and maintained ETL pipelines using Python, Spark, and Azure Cloud to improve accuracy, availability, and performance of critical banking pipelines. Automated code review processes using AI-driven validation tools for transformation logic and deployment workflows. Contributed to internal frameworks integrating with Azure DevOps for automated testing, version control, and environment promotion. Partnered with cross-functional teams to improve delivery efficiency and strengthen code quality across deployments. Implemented Spark/PySpark and SQL-based transformations with validation checks to improve data quality and reliability for business reporting.

Architected and deployed high-throughput Spark/Python workflows in hybrid AWS and Azure environments. Automated ingestion, cleansing, and distribution of sensitive data, applying DevOps principles for traceability, consistency, and fast delivery. Built Python utilities to monitor pipeline health and provide granular validation across distributed services. Integrated components into version-controlled repositories and CI/CD pipelines using Azure DevOps, reducing deployment errors and improving reproducibility.

Delivered a Python automation framework for EUC remediation and Big Data validation supporting regulatory and internal compliance initiatives. Designed toolsets for deployment and monitoring using Apache Spark, Autosys, and Azure-based logging/alerting integrations. Standardized deployment templates and introduced DevOps pipelines for versioning, continuous testing, and rollback patterns.

Built and maintained automated reporting for risk, marketing, and financial stakeholders, improving timeliness and consistency of KPI delivery. Developed data extraction, cleaning, and transformation workflows using Python/R and SQL to produce analytics-ready datasets for reporting and ad-hoc analysis. Designed and delivered Power BI dashboards (risk/portfolio performance) including dataset preparation and measure logic to track key business indicators (e.g., delinquency, origination, performance by segment). Implemented data quality checks and reconciliations across sources to reduce reporting discrepancies and increase reliability for decision-making. Partnered with business teams to translate requirements into actionable analysis, producing recurring insights and executive-ready reporting artifacts.