Himanshu Ravishankar

Applied data analysis and visualization skills to design Tableau dashboards and transform large datasets in Excel, delivering stakeholder-focused insights in a simulated consulting environment.

Developed Power BI dashboards with DAX, Python/SQL ETL pipelines, and ML models to deliver customer insights and achieve 85% churn prediction accuracy.

Built and maintained Azure Data Factory pipelines, ingesting 50M+ records into ADLS Gen2 for real-time and batch processing, orchestrating transformations in Azure Synapse and Databricks, and delivering curated models to Azure SQL, boosting pipeline reliability by 30%.

Led end-to-end Power BI delivery, modeling datasets and designing interactive dashboards adopted by 200+ users, which reduced manual reporting by 40% and accelerated remediation decision-making.

Developed Python (pandas, PySpark) utilities in Azure Databricks to clean, validate, and reconcile high-volume KYC/onboarding datasets, automating anomaly detection, reducing manual data checks by 50%, and improving audit readiness by 20%.

Executed complex SQL queries to validate ETL workflows and ensure data integrity across customer onboarding and KYC compliance datasets, identifying discrepancies and streamlining defect resolution with the development team.

Automated reporting workflows with Power Automate, integrating with Teams, Jira, and email, which reduced manual interventions by 50% and improved SLA adherence with automated task routing and reporting distribution.

Strengthened data quality and governance by enforcing rules on completeness, integrity, and timeliness, automating reconciliations across staging-to-reporting layers, cutting errors by 40% and ensuring compliance.

Delivered Excel-based reconciliation and UAT solutions linked to certified Power BI datasets, improving audit readiness by 20% and cutting ad-hoc reporting turnaround from days to hours for analysts and auditors.

Drove Agile Scrum delivery (2-week sprints), leading backlog refinement, sprint demos, and estimation, consistently delivering greater than 90% of stories on time, improving stakeholder trust and delivery transparency.

Applied a strong analytical mindset to optimize procurement workflows and material tracking within the SAP MM module, supporting supply chain efficiency.

Leveraged Azure Synapse and Data Lake to consolidate IoT sensor, MES, and ERP data into a unified reporting layer, enabling near real-time visibility of machine downtime and production throughput, reducing unplanned stoppages by 10%.

Built Power BI dashboards that combined supplier, production, and logistics data, applying advanced DAX measures for cost-per-unit, cycle time variance, and scrap trend forecasting, supporting leadership in reducing waste by 15%.

Developed SQL procedures and views to integrate supply chain and quality datasets, enabling defect traceability from supplier to assembly line and improving root-cause identification speed by 25%.

Designed Excel-based cost and efficiency trackers with scenario modeling, PivotTables, and Power Pivot, providing finance and operations managers with dynamic simulations of production shifts, lowering overtime costs by 8%.

Applied statistical analysis techniques in Azure Databricks to model defect rates and predict high-risk production batches; findings were used by quality teams to proactively intervene and reduce rework by 12%.

Collaborated with engineering and supply chain teams to model star-schema datasets specific to manufacturing KPIs, ensuring consistent definitions of throughput, rejection rates, and supplier performance across business units.

Automated executive reporting packs by integrating SQL extracts, Power BI dashboards, and Excel summaries, cutting manual reporting time by 30% and improved timeliness of insights to leadership.

Facilitated Agile and Lean initiatives by maintaining Kanban boards, prioritizing defect resolutions, and translating production issues into actionable analytics deliverables, supporting continuous improvement projects that shortened cycle times by 10%.