Vaishali Bhargava

Senior Data Engineer with 9+ years of experience building and optimizing enterprise data platforms, large-scale ETL/ELT pipelines and cloud data solutions. Strong hands-on Snowflake and SQL expertise with a track record of reducing processing time by 45%, scaling throughput 3.5×, improving data completeness to 99.4%, and supporting platforms with 500M+ records and 10M+ daily transactions. Experienced in data architecture, Teradata-to-Snowflake migration, performance engineering, data quality, observability and technical leadership.

Strong conceptual understanding of modern AWS data-platform patterns, including S3-based data lakes, Glue, Lambda, IAM and Apache Iceberg, with hands-on project experience centered on Snowflake, SQL and S3.

Technology Analyst (ITA) | Lead Data Engineer - TCS Pvt Ltd

(2022-03)

Apple Finance Operations – APAC | Snowflake | SQL | Data Architecture

• Architected and optimized enterprise-scale ETL pipelines processing 10M+ transactional rows daily across 6 downstream reporting feeds.
• Reduced pipeline SLA breaches by 45% by redesigning Snowflake extraction procedures and optimizing COPY INTO and file-format strategies; daily load window reduced from 4.5 hours to 1.25 hours.
• Scaled data throughput 3.5× by optimizing Snowflake warehouse clustering, partitioning and query execution, enabling 35M+ rows/day versus a previous 10M ceiling.
• Improved data completeness from 91% to 99.4% by engineering pre-load validation procedures that detected and quarantined malformed records before ingestion.
• Migrated Teradata workloads to Snowflake by refactoring 14 legacy procedures into Snowflake-native queries with clustering keys and result caching; reduced report generation time by 58% (3.5 min to 90 sec).
• Delivered 100% uptime SLA for critical financial reporting through automated pipeline health checks, alert-driven retry logic and monitoring; zero missed SLA windows over 18 months across 5 critical reports.
• Reduced incident resolution time by 65% by introducing root-cause logging and data-observability standards; MTTR decreased from 6.2 hours to 2.1 hours.
• Authored HLD/LLD documentation for multi-source extraction architecture and mentored 4 engineers on Snowflake optimization and data architecture.

Technology Analyst | Lead Data Engineer - Infosys Pvt Ltd

(2021-05 - 2022-03)

Swiss Telecom (Sunrise + UPC Post-Merger Integration) | Teradata | Advanced SQL | Data Modelling

• Architected 360-degree customer data marts integrating 18M+ Sunrise and UPC subscriber records to support cross-sell/up-sell analytics for Marketing and Finance.
• Optimized geospatial query performance by 90% using denormalized building-level datasets and Teradata NUSI/USI indexing; reduced fibre/cable report SLA from 8 minutes to 47 seconds.
• Accelerated churn-risk scoring pipelines by 55% by designing optimized customer subscription and recurring-charge datasets; reduced weekly model retraining from 11 hours to 5 hours.
• Led change-request impact analysis and SQL optimization coaching; mentored 4 junior engineers on Teradata architecture and performance tuning.

Senior Software Engineer - LTI (Larsen & Toubro Infotech)

(2017-02 - 2021-05)

Income Tax Department, Government of India | Teradata | SAS | Data Warehouse

• Architected and built multiple Teradata data marts for a centralized warehouse serving 500M+ taxpayer records across TDS, ITBA, E-filing and OLTAS systems.
• Designed a unified 360-degree Taxpayer View supporting national GIS and BI dashboards.
• Architected a SAS ETL Audit Framework with proactive alerting and automated MIS reporting, reducing daily manual operations oversight by 3.5 hours.
• Improved citizen-facing query performance by 62% using physicalized views in Teradata; reduced Taxpayer Portal P95 latency from 8.1 seconds to 3.1 seconds across 10M+ annual sessions.
• Engineered an address-resolution module that improved notification delivery accuracy to 98.7% versus ~84% previously.
• Designed a modular, reusable ETL framework adopted across 6 data marts, increasing reusability by 40% and reducing new-mart delivery time from 3 weeks to 1.8 weeks.

B.E. - Computer Science