Nikhil Gokhale

•Designed distributed warehouse structures in Snowflake and AWS Redshift integrating surrogate keys, SCD logic, and partitioning strategies to support high-volume, multi-source analytics. •Architected Tableau and Power BI reporting ecosystems featuring executive scorecards, automated KPI definitions, and real-time SLA monitoring for US business stakeholders. •Built Python-based automation frameworks leveraging Pandas, NumPy, and API integrations to streamline reconciliation, enrichment, and validation reducing manual effort by 65%. •Conducted experimentation and statistical impact assessments including A/B testing, cohort analysis, and uplift modeling to support product release decisions. •Developed forecasting pipelines using advanced predictive techniques and business seasonality patterns improving executive budgeting and capacity planning accuracy. •Enhanced ETL orchestration with error-handling, log capture, dependency management, and incremental refresh achieving 30% greater pipeline reliability. •Collaborated with cross-functional teams using Git, JIRA, and ServiceNow to refine data governance, establish metric definitions, and implement standardized reporting workflows.

•Managed user setup, profiles, and permission sets for over 500+ users across Sales and Support business units, improving access control accuracy and reducing permission-related support tickets by 30%. •Engineered analytical SQL workflows and optimized query logic to accelerate KPI and operational reporting cycles by 40% for finance and operations teams. •Built ETL pipelines using Python, Power Query, and incremental loading routines to automate ingestion from CRM, ERP, marketing, and IoT sources supporting multi-department analytics. •Developed enterprise-grade Power BI dashboards incorporating drill-down analytics, DAX measures, and layered KPI frameworks adopted by senior management for quarterly reviews. •Performed comprehensive data profiling, cleansing, and schema refinement achieving 98% accuracy across historical customer and revenue datasets. •Applied regression, hypothesis testing, and time-series models to generate demand forecasts improving planning precision by 22%. •Created structured data warehouse models using star and snowflake schemas to support scalable dashboard consumption and reduce redundant transformations. •Delivered ad-hoc analytical studies and cohort evaluations enabling product and marketing teams to identify behavior patterns, high-value customer segments, and funnel drop-offs.