Sandeep Bhaskar

• Architect and maintain scalable, production-grade data pipelines on GCP using BigQuery, ensuring efficient ingestion, transformation, and serving layers for analytics use cases.
• Orchestrate end-to-end data workflows using Apache Airflow, implementing DAGs for scheduling, dependency management, monitoring, and failure handling.
• Develop modular, reusable, and testable data transformation models using DBT, following ELT best practices with version control and environment management.
• Design and implement optimized data models (fact/dimension schemas) to support high-performance analytical queries and downstream BI consumption.
• Build and manage ETL/ELT pipelines integrating structured and semi-structured data from multiple sources, ensuring data quality, consistency, and lineage.
• Write optimized SQL queries and leverage Python for data processing, transformation logic, and performance tuning of large-scale datasets.
• Implement data validation, error handling, and logging mechanisms to ensure reliability and robustness of data pipelines.
• Optimize BigQuery performance using partitioning, clustering, query optimization techniques, and cost-efficient data processing strategies.
• Collaborate with BI teams to deliver curated, analytics-ready datasets that power Tableau dashboards and enable advanced reporting and time-series analysis

Partnered with project managers, team leads, and stakeholders to translate business requirements into scalable, data-driven solutions aligned with organizational goals.

Designed and delivered interactive KPI dashboards with advanced visualizations, improving visibility into performance metrics and enabling faster, insight-driven decision-making.

Conducted in-depth time-series analysis (YTD, QTD, MTD, YoY) to identify trends, measure growth, and support strategic planning.

Leveraged advanced Tableau capabilities (LOD expressions, parameters, sets, groups, calculated fields, and table calculations) to build dynamic dashboards, including top/bottom performance analysis for targeted business insights.

• Optimized dashboard performance by utilizing best practices in data extraction, filtering, and aggregation techniques.
• Increased user adoption rates by creating visually appealing and user-friendly interface designs for Tableau dashboards.