Sahil Bhatia

Senior Data Engineer with 4.8+ years architecting and scaling cloud-native data platforms on Snowflake, AWS, and dbt. Specialized in real-time CDC ingestion, Medallion Architecture, data quality frameworks, and cost-optimized warehouse design, processing 100M+ records daily. Proven record of cutting infrastructure spend, reducing pipeline latency from hours to near real-time, and partnering with analytics, BI, and AI teams to ship reliable data products.

Senior Data Engineer at SHL (2026-03 – Present)

• Led the migration of enterprise data pipelines from Amazon Redshift to Snowflake, modernizing the organization's core analytics platform and reduced data latency from 24 hours to near real-time by architecting CDC ingestion using AWS DMS, Snowpipe, and Dynamic Tables.
• Architected a multi-region Snowflake environment spanning US, EU, and AU, with isolated Dev, QA, UAT, and Prod environments to support global enterprise operations.
• Partnered with the Snowflake team to implement Disaster Recovery on Snowflake Enterprise Edition, configuring primary and secondary accounts for each region to ensure business continuity.
• Designed a Medallion Architecture (Bronze, Silver, Gold layers) and a dbt Core + Elementary Data Quality framework, standardizing data modeling and validation across critical pipelines.
• Built a Snowflake Cortex agent powering a self-service portal that lets account managers access business data using natural language, removing dependency on ad hoc reporting requests.
• Implemented a GDPR-compliant data anonymization framework and a Liquibase-based CI/CD pipeline for environment- and region-specific schema deployments.

Data Engineer at Birdeye (2024-10 – 2026-03)

• Led migration from Fivetran to a Snowflake Kafka Connector using Debezium, automating ingestion of 20M+ daily records and backfilling several billion historical records from MySQL to Snowflake.
• Designed and built end-to-end ETL/ELT pipelines ingesting data from AWS S3, MongoDB, REST APIs, and relational databases using CDC and batch processing into Snowflake and S3-based data lakes.
• Built Streamlit tools integrated with Snowflake that cut manual effort by 90% for field mapping, view/dynamic table creation, and data comparison during the Fivetran-to-Airbyte migration.
• Implemented pipeline monitoring/alerting via Snowflake Python stored procedures and RingCentral, and optimized merge tasks and clustering keys, cutting warehouse compute costs by approximately 37%.
• Partnered with BI, analytics, and AI teams to gather requirements and deliver trusted data sources for Tableau and downstream reporting.

Data Engineer at Pitney Bowes (2022-02 – 2024-09)

• Built enterprise-grade ETL pipelines using PySpark, SnapLogic, SQL, and AWS (S3, Lambda, EMR) to process high-volume parcel shipping and tracking datasets.
• Developed Power BI dashboards and automated Python-based email reporting for daily/weekly tracking of Snowflake credit usage, storage, and query performance.
• Reduced Snowflake infrastructure costs by 8-12% per warehouse, saving over $7,000 per month through usage analysis and tuning.
• Designed alerting systems to flag warehouse/query anomalies, long-running queries, and compute misuse, improving platform stability.

B.Tech in Computer Science and Engineering – J.C. Bose University of Science & Technology (YMCA) (2018-07 – 2022-06)