Pune District, Maharashtra, India
Data is only as valuable as the decisions it enables. I build analytics systems that turn complex, large-scale data into clear, actionable insights for sales teams, leadership, and business stakeholders who need answers fast. My work spans the full lifecycle — from designing dbt-based models and dimensional schemas to deploying Streamlit applications and Tableau dashboards that drive daily decision-making. I focus on scale and usability: optimizing pipelines processing billions of records, embedding data quality and access controls, and enabling natural language querying using Snowflake Cortex (LLM). In my recent work, I’ve supported analytics used by 4,000+ stakeholders, improved dashboard performance by ~85–90%, and reduced query turnaround times significantly through system and pipeline optimizations. My goal is simple — reduce the time between a business question and a confident, data-backed answer. Tech Stack: SQL (Snowflake, SparkSQL, Hive) · Python · dbt · Airflow · Streamlit · Tableau · Apache Spark · AWS (S3, EC2, EMR, Redshift)
• Architected and deployed GTM analytics data products - used by 4,000+ stakeholders - to unify sales and leadership reporting, enabling monitoring of revenue, consumption, and product adoption to identify expansion opportunities. • Modernized legacy global reporting infrastructure by migrating dashboards from Tableau to Streamlit and re-engineering KPI logic into dbt-based SQL models — reducing load times significantly and contributing directly to significant annual licensing cost savings • Designed and deployed a centralized dbt pipeline and Streamlit dashboard powering a gamified sales training system, enabling self-serve compliance monitoring and eliminating the vast majority of ad-hoc support queries. • Collaborated with global business stakeholders in an Agile, sprint-based delivery model using JIRA to drive iterative enhancements across dashboards and datasets, improving usability, scalability, and metric consistency.
• Led a cross-functional team through sprint-based delivery cycles, owning end-to-end execution from requirement gathering to production analytics deployment. • Built a patient-level analytics platform by integrating multi-source datasets and modeling end-to-end patient journeys, enabling identification of critical drop-offs and targeted interventions. • Optimized large-scale data pipelines processing billions of records using Python-based batching, significantly reducing runtime and eliminating memory-related failures.
• Engineered end-to-end Spark SQL pipelines with automated data quality and compliance checks for large-scale time-series data, enabling analysis of prescriber trends and supporting targeted field engagement across geographies. • Ensured zero-defect UAT outcomes across multiple product launches by building robust data pipelines and reporting dashboards, delivering accurate and reliable data for business-critical reporting. • Mentored junior developers through structured code reviews and engineering best practices, improving delivery quality and reducing rework across the team. • Optimized S3 backup workflows by implementing selective table-level backups, significantly reducing execution time and improving environment refresh efficiency while maintaining SLA adherence.