Akash Deep Kumar

Power BI & Azure Databricks (Client: UBS)

• Architected complex Star and Snowflake schemas supporting high-cardinality financial data across multi-level hierarchies (Asset Class, Capability, Strategy, Portfolio, Share Class).
• Implemented Composite Models and Aggregations to balance DirectQuery performance on Azure Databricks Delta Lake with in-memory Import mode speed for historical analytics.
• Leveraged Calculation Groups in Tabular Editor to eliminate measure proliferation, standardizing time-intelligence calculations (MTD, QTD, YTD, YoY, QoQ) across hundreds of performance metrics.
• Developed complex, non-additive DAX measures using context transition, dynamic segmentation, and iterator functions to calculate risk metrics and relative returns.
• Implemented Dynamic Measure Selection and Field Parameters, allowing executives to seamlessly swap visual contexts between absolute return, benchmark return, and AUM across flexible time horizons.
• Handled complex Time-Intelligence logic across custom financial calendars (non-standard 4-4-5 / 4-5-4 reporting periods) and multi-currency/fx conversions.
• Optimized query performance and reduced dashboard load times by up to 60% using DAX Studio, VertiPaq Analyzer, and Tabular Editor to prune unnecessary columns, reduce memory footprint, and optimize DAX query execution plans.
• Conducted detailed bottleneck analysis using Power BI Performance Analyzer and tuned query folding in Power Query / M-code to push heavy transformations upstream to Azure Databricks.
• Designed and enforced fine-grained Row-Level Security (RLS) and Object-Level Security (OLS) dynamic roles to restrict sensitive financial AUM and performance data based on user roles and business units.
• Managed Enterprise ALM using Power BI Deployment Pipelines (Development, Test, Production) and XMLA endpoints for seamless model versioning, automated deployment, and CI/CD integration.
• Configured automated dataset refresh strategies, incremental refreshes, and API-triggered dataset refreshes via Azure Data Factory and Power BI REST APIs.
• Designed, optimized, and deployed custom data models, tables, and views in Azure Databricks to serve as the high-performance backend data source for Power BI dashboards.
• Built scalable ETL/ELT pipelines in Databricks using PySpark / SQL to transform complex, multi-asset banking and financial datasets into analytical schemas.
• Ensured data accuracy, integrity, and query performance tuning between Azure Databricks and Power BI using DirectQuery and Import modes.
• Maintained data pipelines and database objects in alignment with strict banking compliance, governance, and reporting standards.

Power BI & Data Analytics (Client: Ford)

• Designed and deployed enterprise-grade dashboards using Power BI to support business decision-making.
• Built data models with calculated columns and measures using DAX, enabling deep analytical capabilities.
• Automated ETL pipelines using Power Query, integrating data from SharePoint, MySQL, and REST APIs.
• Implemented Row-Level Security (RLS) and managed Power BI Service for secure report sharing and scheduling.
• Created paginated reports and interactive visuals with slicers, filters, and drill-through functionality.
• Integrated Power BI with SharePoint Online, using lists and libraries for real-time data updates.
• Developed SQL-based views and stored procedures to optimize data retrieval for reporting.
• Enabled API-based data enrichment, parsing JSON responses and integrating external datasets.
• Supported end-user training and adoption of Power BI dashboards across business units.

Automation & Data Analytics (Client: Google Cloud)

• Developed custom automation scripts using Google Apps Script to streamline data collection and reporting workflows.
• Created interactive dashboards in Looker Studio for internal performance tracking and operational insights.
• Integrated Google Sheets with Apps Script to automate data validation, formatting, and email notifications.
• Used PLX Tool for performance metrics visualization and reporting across teams.
• Built parameterized reports in Looker Studio with dynamic filtering and drill-down capabilities.
• Scheduled automated delivery of reports via email and embedded dashboards in internal portals.

Google Ecosystem and Visualisation (Client: Google Cloud)

• Designed and deployed client-facing dashboards using Looker Studio, integrating data from BigQuery, Google Sheets, and SQL databases.
• Created multi-source blended reports for cross-functional analysis and KPI tracking.
• Implemented LookML models in Looker to define reusable metrics and dimensions for scalable reporting.
• Developed custom metrics and calculated fields to support business-specific analytics.
• Automated report scheduling and distribution using Looker Studio’s built-in capabilities.
• Built interactive dashboards with drill-downs, filters, and embedded visualizations for stakeholder engagement.
• Used Google Apps Script to automate data ingestion, transformation, and report generation.
• Collaborated with cross-functional teams to define reporting requirements and deliver tailored solutions.
• Optimized dashboard performance by minimizing query load and leveraging caching strategies.
• Created data governance workflows to ensure accuracy and consistency across reports.