Rajdeep Jinegar

Architected and operationalized a medallion (Bronze/Silver/Gold) data architecture in Microsoft Fabric, owning the Bronze and Silver layers end-to-end and establishing the foundation for all downstream analytics across 5 business domains. Owned CI/CD process for Fabric artifacts using Git integration, DEV/PROD environments, and controlled deployment workflows to reduce production risk during report and data model changes. Established security and governance controls enforcing clear access separation across raw, transformed, and curated layers, supporting compliance requirements for sensitive (healthcare or patient) data.

Database and Analytics Specialist at Lifemark Health Group (2024-08 – Present)

• Architected and operationalized a medallion (Bronze/Silver/Gold) data architecture in Microsoft Fabric, owning the Bronze and Silver layers end-to-end and establishing the foundation for all downstream analytics across 5 business domains.
• Owned CI/CD process for Fabric artifacts using Git integration, DEV/PROD environments, and controlled deployment workflows to reduce production risk during report and data model changes.
• Established security and governance controls enforcing clear access separation across raw, transformed, and curated layers, supporting compliance requirements for sensitive (healthcare or patient) data.
• Monitored capacity health via the Fabric Capacity Metrics app, diagnosing CU consumption patterns, throttling and smoothing behaviour, and per-engine utilization to right-size reserved capacity and prevent overage.
• Architected a full-funnel growth analytics framework across 460+ clinics, identifying scheduling-to-attendance as the largest revenue leakage point; redirected GTM strategy from acquisition-heavy spend to conversion optimization, driving an 11% increase in attendance and a 6% lift in monthly revenue.
• Built clinician ramp-up and cohort analytics, tracking productivity (booked hours and patients per day) to identify underperforming providers and optimize onboarding efficiency.
• Modernized legacy SQL infrastructure by rewriting 60+ stored procedures, improving query performance by 99.9%, reducing execution time from 2 hours to a few minutes and drastically reducing reporting latency. Migrated reports from SSRS to PowerBI and made real-time reporting possible with the optimized stored procedures.
• Architected and implemented an enterprise data warehouse using the Kimball dimensional modelling approach, designing star schemas and slowly changing dimensions (SCD-2) to integrate clinical, HR, and financial data during migration from on-prem SQL Server to Microsoft Fabric.

Data Analyst and Engineer at Digikentro (2021-11 – 2024-04)

• Utilized Python to automate data extraction processes, reducing data retrieval time by 30%. Developed custom scripts and scheduled jobs using libraries such as Pandas and Requests to efficiently gather data from various sources, including APIs and databases.
• Developed and optimized SQL queries to extract, transform, and load (ETL) large volumes of data, resulting in a 30% reduction in query execution time.
• Collected data from different sources, such as CSV files, databases, JSON, and APIs, and assisted users in extracting and interpreting data from multiple sources, generating insightful summary reports. Streamlined data collection processes, reducing time by 20% and improving data accuracy.
• Integrated Python scripts with SQL queries successfully, including the use of joins, CTEs, TEMP TABLES, Window Functions, Partitions, and Subqueries, leading to a 30% improvement in data processing speed and enabling seamless analysis of large datasets, leveraging SQL Alchemy.

Master of Engineering in Computer Engineering – Carleton University (2022-01 – 2023-10)

Bachelor of Science in Computer Science – Gujarat Technological University (2017-08 – 2021-07)